diff --git a/openadapt_ml/training/grpo/reward.py b/openadapt_ml/training/grpo/reward.py
index 48caf26..c8b3dc0 100644
--- a/openadapt_ml/training/grpo/reward.py
+++ b/openadapt_ml/training/grpo/reward.py
@@ -15,7 +15,11 @@
 
 from __future__ import annotations
 
+import io
 import logging
+import math
+
+from PIL import Image
 
 logger = logging.getLogger(__name__)
 
@@ -31,6 +35,18 @@ class MilestoneEvaluationError(RuntimeError):
     """
 
 
+def _unit_interval(value: object, name: str) -> float:
+    """Return a finite numeric value in the closed unit interval."""
+    if (
+        isinstance(value, bool)
+        or not isinstance(value, (int, float))
+        or not math.isfinite(value)
+        or not 0.0 <= value <= 1.0
+    ):
+        raise ValueError(f"{name} must be a finite number in [0, 1]")
+    return float(value)
+
+
 def binary_task_success(score: float, threshold: float = 0.5) -> float:
     """Convert evaluator score to binary reward.
 
@@ -41,7 +57,9 @@ def binary_task_success(score: float, threshold: float = 0.5) -> float:
     Returns:
         1.0 if score >= threshold, else 0.0.
     """
-    return 1.0 if score >= threshold else 0.0
+    measured_score = _unit_interval(score, "score")
+    measured_threshold = _unit_interval(threshold, "threshold")
+    return 1.0 if measured_score >= measured_threshold else 0.0
 
 
 def compute_group_advantages(rewards: list[float]) -> list[float]:
@@ -64,8 +82,13 @@ def compute_group_advantages(rewards: list[float]) -> list[float]:
     if n == 0:
         return []
 
-    mean = sum(rewards) / n
-    variance = sum((r - mean) ** 2 for r in rewards) / n
+    measured_rewards = [
+        _unit_interval(reward, f"rewards[{index}]")
+        for index, reward in enumerate(rewards)
+    ]
+
+    mean = sum(measured_rewards) / n
+    variance = sum((reward - mean) ** 2 for reward in measured_rewards) / n
     std = variance**0.5
     eps = 1e-8
 
@@ -73,7 +96,7 @@ def compute_group_advantages(rewards: list[float]) -> list[float]:
     if std < eps:
         return [0.0] * n
 
-    return [(r - mean) / (std + eps) for r in rewards]
+    return [(reward - mean) / (std + eps) for reward in measured_rewards]
 
 
 def evaluate_milestones_screenshot(
@@ -122,6 +145,17 @@ def evaluate_milestones_screenshot(
             "milestones, so there is nothing to score. This is not a reward "
             "of 0.0."
         )
+    if not screenshot_bytes:
+        raise MilestoneEvaluationError(
+            "Screenshot milestone evaluation requires non-empty screenshot bytes"
+        )
+    try:
+        with Image.open(io.BytesIO(screenshot_bytes)) as screenshot:
+            screenshot.verify()
+    except Exception as exc:
+        raise MilestoneEvaluationError(
+            "Screenshot milestone evaluation requires decodable image evidence"
+        ) from exc
 
     # Only evaluate screenshot-type milestones locally
     screenshot_milestones = [
@@ -154,7 +188,7 @@ def evaluate_milestones_screenshot(
                 "silently depress the reward."
             )
         try:
-            success, _confidence = vlm_judge(
+            success, confidence = vlm_judge(
                 screenshot_bytes,
                 description,
                 model=vlm_model,
@@ -165,6 +199,18 @@ def evaluate_milestones_screenshot(
                 f"VLM judge failed on milestone {getattr(ms, 'name', '?')!r}: "
                 f"{exc}. A judge failure is not a failed milestone."
             ) from exc
+        if not isinstance(success, bool):
+            raise MilestoneEvaluationError(
+                f"VLM judge returned a non-boolean success value for milestone "
+                f"{getattr(ms, 'name', '?')!r}"
+            )
+        try:
+            _unit_interval(confidence, "VLM judge confidence")
+        except ValueError as exc:
+            raise MilestoneEvaluationError(
+                f"VLM judge returned invalid confidence for milestone "
+                f"{getattr(ms, 'name', '?')!r}"
+            ) from exc
         if success:
             passed += 1
         logger.debug(
diff --git a/openadapt_ml/training/grpo/rollout_collector.py b/openadapt_ml/training/grpo/rollout_collector.py
index 56e5af1..affccc6 100644
--- a/openadapt_ml/training/grpo/rollout_collector.py
+++ b/openadapt_ml/training/grpo/rollout_collector.py
@@ -12,6 +12,7 @@
 from __future__ import annotations
 
 import logging
+import math
 import random
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, Callable
@@ -30,6 +31,10 @@
 logger = logging.getLogger(__name__)
 
 
+class RolloutCollectionError(RuntimeError):
+    """The environment did not produce a measurable rollout."""
+
+
 @dataclass
 class Rollout:
     """Complete episode rollout with reward.
@@ -155,8 +160,22 @@ def collect_group(
                 task_id=task_id,
             )
 
+            if not steps:
+                raise RolloutCollectionError(
+                    f"Task {task_id!r} produced no rollout steps"
+                )
+
             # Extract terminal score from the last step's reward
-            raw_score = steps[-1].reward if steps else 0.0
+            raw_score = steps[-1].reward
+            if (
+                isinstance(raw_score, bool)
+                or not isinstance(raw_score, (int, float))
+                or not math.isfinite(raw_score)
+                or not 0.0 <= raw_score <= 1.0
+            ):
+                raise RolloutCollectionError(
+                    f"Task {task_id!r} returned invalid terminal score {raw_score!r}"
+                )
             reward = binary_task_success(raw_score)
 
             # CR-01: Extract task instruction from the environment's
diff --git a/openadapt_ml/training/grpo/trainer.py b/openadapt_ml/training/grpo/trainer.py
index 51060e4..a6f1ca6 100644
--- a/openadapt_ml/training/grpo/trainer.py
+++ b/openadapt_ml/training/grpo/trainer.py
@@ -30,7 +30,9 @@
 from __future__ import annotations
 
 import io
+import json
 import logging
+import math
 import re
 import time
 from pathlib import Path
@@ -60,6 +62,36 @@
 DEFAULT_SCREEN_SIZE: tuple[int, int] = (1920, 1080)
 
 
+class ActionParseError(ValueError):
+    """Model output did not contain one valid explicit action."""
+
+
+class RolloutLossError(RuntimeError):
+    """A rollout did not contain the evidence required to compute loss."""
+
+
+def _strict_json_object(pairs: list[tuple[str, Any]]) -> dict[str, Any]:
+    """Build one JSON object while refusing duplicate keys."""
+    result: dict[str, Any] = {}
+    for key, value in pairs:
+        if key in result:
+            raise ActionParseError(f"JSON action contains duplicate key: {key!r}")
+        result[key] = value
+    return result
+
+
+def _normalized_point_to_pixels(
+    x: float,
+    y: float,
+    screen_size: tuple[int, int],
+) -> tuple[int, int]:
+    """Map inclusive normalized coordinates to valid viewport indices."""
+    width, height = screen_size
+    if width <= 0 or height <= 0:
+        raise ActionParseError("Screen dimensions must be positive")
+    return min(int(x * width), width - 1), min(int(y * height), height - 1)
+
+
 # ---------------------------------------------------------------------------
 # Policy gradient loss
 # ---------------------------------------------------------------------------
@@ -166,44 +198,136 @@ def _parse_vlm_output_to_action(
     # Log raw output for debugging zero-reward issues
     logger.debug("Parsing VLM output (%d chars): %.200s", len(text), text)
 
-    # Extract action from "Thought: ...\nAction: ..." format (SFT output)
-    action_match = re.search(r"Action:\s*(.+)", text, re.IGNORECASE)
-    if action_match:
-        text = action_match.group(1).strip()
-
-    # Try JSON format: {"action_type": "click", "coordinate": [x, y]}
-    json_match = re.search(r'\{[^}]*"action_type"[^}]*\}', text)
-    if json_match:
+    # Accept an optional thought followed by exactly one explicit action envelope.
+    action_markers = list(re.finditer(r"\bAction:\s*", text, re.IGNORECASE))
+    if len(action_markers) > 1:
+        raise ActionParseError("Model output contains more than one Action envelope")
+    if action_markers:
+        marker = action_markers[0]
+        prefix = text[: marker.start()].strip()
+        if prefix and not re.match(r"^Thought:\s*", prefix, re.IGNORECASE):
+            raise ActionParseError("Only a Thought may precede the Action envelope")
+        text = text[marker.end() :].strip()
+
+    # JSON must occupy the complete action envelope.
+    if text.startswith("{") or text.endswith("}"):
+        try:
+            action_data = json.loads(text, object_pairs_hook=_strict_json_object)
+        except (TypeError, json.JSONDecodeError) as exc:
+            raise ActionParseError("JSON action envelope is malformed") from exc
+        if not isinstance(action_data, dict):
+            raise ActionParseError("JSON action envelope must be an object")
+
+        atype_value = action_data.get("action_type")
+        if not isinstance(atype_value, str):
+            raise ActionParseError("JSON action requires an action_type string")
+        atype = atype_value.lower()
         try:
-            import json as _json
-
-            action_data = _json.loads(json_match.group())
-            atype = action_data.get("action_type", "").lower()
             coord = action_data.get("coordinate", action_data.get("coords", []))
-            if atype == "click" and len(coord) >= 2:
+            if atype == "click":
+                coordinate_fields = {"coordinate", "coords"} & action_data.keys()
+                mode_fields = {
+                    "coordinate_mode",
+                    "coordinate_space",
+                } & action_data.keys()
+                if len(coordinate_fields) != 1:
+                    raise ActionParseError(
+                        "Click requires exactly one coordinate field"
+                    )
+                if len(mode_fields) > 1:
+                    raise ActionParseError(
+                        "Click permits at most one coordinate-mode field"
+                    )
+                allowed_fields = {
+                    "action_type",
+                    *coordinate_fields,
+                    *mode_fields,
+                }
+                if action_data.keys() - allowed_fields:
+                    raise ActionParseError("Click action contains unsupported fields")
+                if not isinstance(coord, (list, tuple)) or len(coord) != 2:
+                    raise ActionParseError("Click requires exactly two coordinates")
                 x_val, y_val = float(coord[0]), float(coord[1])
-                # Handle both normalized (0-1) and pixel coordinates
-                if x_val <= 1.0 and y_val <= 1.0:
-                    x_val, y_val = x_val * width, y_val * height
+                if not math.isfinite(x_val) or not math.isfinite(y_val):
+                    raise ActionParseError("Click coordinates must be finite")
+
+                coordinate_mode = action_data.get(
+                    "coordinate_mode", action_data.get("coordinate_space")
+                )
+                if coordinate_mode is not None:
+                    coordinate_mode = str(coordinate_mode).lower()
+                normalized = 0.0 <= x_val <= 1.0 and 0.0 <= y_val <= 1.0
+                pixels = 0.0 <= x_val < width and 0.0 <= y_val < height
+                if coordinate_mode == "normalized":
+                    if not normalized:
+                        raise ActionParseError(
+                            "Normalized click coordinates must be in [0, 1]"
+                        )
+                    x_px, y_px = _normalized_point_to_pixels(x_val, y_val, screen_size)
+                    return BenchmarkAction(type="click", x=x_px, y=y_px)
+                elif coordinate_mode in ("pixel", "pixels"):
+                    if not pixels:
+                        raise ActionParseError(
+                            "Pixel click coordinates must be inside the viewport"
+                        )
+                elif coordinate_mode is None and normalized:
+                    x_px, y_px = _normalized_point_to_pixels(x_val, y_val, screen_size)
+                    return BenchmarkAction(type="click", x=x_px, y=y_px)
+                elif (
+                    coordinate_mode is None
+                    and 1.0 < x_val < width
+                    and 1.0 < y_val < height
+                ):
+                    pass
+                else:
+                    raise ActionParseError(
+                        "Click coordinates are out of range or mix normalized and "
+                        "pixel units"
+                    )
                 return BenchmarkAction(type="click", x=int(x_val), y=int(y_val))
             if atype == "type":
-                return BenchmarkAction(type="type", text=action_data.get("text", ""))
+                if action_data.keys() != {"action_type", "text"}:
+                    raise ActionParseError(
+                        "TYPE requires exactly action_type and explicit string text fields"
+                    )
+                if "text" not in action_data or not isinstance(
+                    action_data["text"], str
+                ):
+                    raise ActionParseError(
+                        "TYPE requires an explicit string text field"
+                    )
+                return BenchmarkAction(type="type", text=action_data["text"])
             if atype in ("done", "wait"):
+                if action_data.keys() != {"action_type"}:
+                    raise ActionParseError(
+                        f"{atype.upper()} does not accept additional fields"
+                    )
                 return BenchmarkAction(type=atype)
-        except Exception:
-            pass  # Fall through to regex parsing
+        except ActionParseError:
+            raise
+        except (TypeError, ValueError):
+            raise ActionParseError("JSON action contains invalid values") from None
+        raise ActionParseError(f"Unsupported JSON action type: {atype!r}")
 
     # CLICK(x=..., y=...)
-    m = re.search(r"CLICK\(x=(-?[\d.]+),\s*y=(-?[\d.]+)\)", text, re.IGNORECASE)
+    m = re.fullmatch(r"CLICK\(x=(-?[\d.]+),\s*y=(-?[\d.]+)\)", text, re.IGNORECASE)
     if m:
-        x_frac = max(0.0, min(1.0, float(m.group(1))))
-        y_frac = max(0.0, min(1.0, float(m.group(2))))
-        return BenchmarkAction(
-            type="click", x=int(x_frac * width), y=int(y_frac * height)
-        )
+        x_frac = float(m.group(1))
+        y_frac = float(m.group(2))
+        if not (
+            math.isfinite(x_frac)
+            and math.isfinite(y_frac)
+            and 0.0 <= x_frac <= 1.0
+            and 0.0 <= y_frac <= 1.0
+        ):
+            raise ActionParseError(
+                "CLICK coordinates must be finite fractions in [0, 1]"
+            )
+        x_px, y_px = _normalized_point_to_pixels(x_frac, y_frac, screen_size)
+        return BenchmarkAction(type="click", x=x_px, y=y_px)
 
     # TYPE(text="..." or '...')
-    m = re.search(
+    m = re.fullmatch(
         r"""TYPE\(text=["']([^"'\\]*(?:\\.[^"'\\]*)*)["']\)""", text, re.IGNORECASE
     )
     if m:
@@ -212,14 +336,13 @@ def _parse_vlm_output_to_action(
         )
         return BenchmarkAction(type="type", text=typed_text)
 
-    if re.search(r"\bWAIT\s*\(\s*\)", text, re.IGNORECASE):
+    if re.fullmatch(r"WAIT\s*\(\s*\)", text, re.IGNORECASE):
         return BenchmarkAction(type="wait")
 
-    if re.search(r"\bDONE\s*\(\s*\)", text, re.IGNORECASE):
+    if re.fullmatch(r"DONE\s*\(\s*\)", text, re.IGNORECASE):
         return BenchmarkAction(type="done")
 
-    logger.warning("Could not parse VLM output: %s. Defaulting to DONE.", text)
-    return BenchmarkAction(type="done")
+    raise ActionParseError(f"Could not parse VLM output: {text[:200]!r}")
 
 
 def _format_action_as_text(
@@ -227,25 +350,41 @@ def _format_action_as_text(
     screen_size: tuple[int, int] = DEFAULT_SCREEN_SIZE,
 ) -> str:
     """Convert a BenchmarkAction back to DSL text for log-prob computation."""
-    action_type = getattr(action, "type", "done")
+    action_type = getattr(action, "type", None)
     width, height = screen_size
 
     if action_type == "click":
-        x_px = getattr(action, "x", 0) or 0
-        y_px = getattr(action, "y", 0) or 0
+        x_px = getattr(action, "x", None)
+        y_px = getattr(action, "y", None)
+        if x_px is None or y_px is None:
+            raise ActionParseError("Cannot format a click without x and y coordinates")
+        x_px = float(x_px)
+        y_px = float(y_px)
+        if not (
+            math.isfinite(x_px)
+            and math.isfinite(y_px)
+            and 0.0 <= x_px < width
+            and 0.0 <= y_px < height
+        ):
+            raise ActionParseError("Cannot format click coordinates outside the screen")
         x_frac = x_px / width if width > 0 else 0.0
         y_frac = y_px / height if height > 0 else 0.0
         return f"CLICK(x={x_frac:.2f}, y={y_frac:.2f})"
 
     if action_type == "type":
-        text = getattr(action, "text", "") or ""
+        text = getattr(action, "text", None)
+        if not isinstance(text, str):
+            raise ActionParseError("Cannot format TYPE without explicit string text")
         escaped = text.replace("\\", "\\\\").replace('"', '\\"')
         return f'TYPE(text="{escaped}")'
 
     if action_type == "wait":
         return "WAIT()"
 
-    return "DONE()"
+    if action_type == "done":
+        return "DONE()"
+
+    raise ActionParseError(f"Cannot format unsupported action type: {action_type!r}")
 
 
 # Public aliases (the underscore-prefixed versions are kept for backwards compat)
@@ -334,6 +473,7 @@ def __init__(self, config: GRPOConfig) -> None:
         self._collector: GRPORolloutCollector | None = None
         self._step: int = 0
         self._task_configs: dict[str, Any] = {}
+        self._training_metrics: list[dict[str, Any]] = []
 
         # Load task configs from --task-dir if specified
         if config.task_dir:
@@ -559,6 +699,7 @@ def train(self) -> str:
         logger.info("  Task configs loaded: %d", len(self._task_configs))
         logger.info("  Rollouts/step: %d", self._config.num_rollouts_per_step)
         logger.info("  Training steps: %d", self._config.num_training_steps)
+        self._training_metrics.clear()
 
         # Setup
         self._model, self._processor = _load_model_and_processor(self._config)
@@ -604,6 +745,7 @@ def train(self) -> str:
                     "step_time": time.time() - step_start,
                 }
             )
+            self._training_metrics.append(dict(metrics))
             logger.info(
                 "Step %d/%d: reward=%.2f loss=%.4f time=%.1fs",
                 step + 1,
@@ -627,9 +769,24 @@ def train(self) -> str:
         logger.info("Training complete. Final checkpoint: %s", final_path)
         return final_path
 
-    def _training_step(self, rollouts: list[Rollout]) -> dict[str, float]:
+    @property
+    def training_metrics(self) -> tuple[dict[str, Any], ...]:
+        """Return a snapshot of the measured result for each training step."""
+        return tuple(dict(metrics) for metrics in self._training_metrics)
+
+    def _training_step(self, rollouts: list[Rollout]) -> dict[str, Any]:
         """Single GRPO gradient step from a group of rollouts."""
+        if not rollouts:
+            raise RolloutLossError("Cannot train from an empty rollout group")
         rewards = [r.reward for r in rollouts]
+        if any(
+            isinstance(reward, bool)
+            or not isinstance(reward, (int, float))
+            or not math.isfinite(reward)
+            or not 0.0 <= reward <= 1.0
+            for reward in rewards
+        ):
+            raise RolloutLossError("Cannot train from an invalid rollout reward")
         advantages = compute_group_advantages(rewards)
         reward_mean = sum(rewards) / len(rewards) if rewards else 0.0
 
@@ -648,11 +805,18 @@ def _training_step(self, rollouts: list[Rollout]) -> dict[str, float]:
             loss_val = self._compute_rollout_loss(
                 rollout, advantage, loss_scale=1.0 / n
             )
+            if not math.isfinite(loss_val):
+                self._optimizer.zero_grad()
+                raise RolloutLossError("Rollout produced a non-finite loss")
             total_loss += loss_val
 
-        torch.nn.utils.clip_grad_norm_(
+        gradient_norm = torch.nn.utils.clip_grad_norm_(
             [p for p in self._model.parameters() if p.requires_grad], max_norm=1.0
         )
+        gradient_norm_value = float(gradient_norm)
+        if not math.isfinite(gradient_norm_value):
+            self._optimizer.zero_grad()
+            raise RolloutLossError("Rollout produced non-finite gradients")
         self._optimizer.step()
 
         return {
@@ -661,6 +825,8 @@ def _training_step(self, rollouts: list[Rollout]) -> dict[str, float]:
             "skipped": False,
             "num_rollouts": len(rollouts),
             "num_gradient_terms": n,
+            "gradient_norm": gradient_norm_value,
+            "optimizer_step_applied": True,
         }
 
     def _compute_rollout_loss(
@@ -684,26 +850,37 @@ def _compute_rollout_loss(
 
         # Gather valid steps
         valid_steps = []
-        for step in rollout.steps:
+        for step_index, step in enumerate(rollout.steps):
             obs = getattr(step, "observation", None)
             action = getattr(step, "action", None)
             if obs is None or action is None:
-                continue
+                raise RolloutLossError(
+                    f"Rollout {rollout.task_id!r} step {step_index} is missing "
+                    "its observation or action"
+                )
             screenshot = getattr(obs, "screenshot", None)
             if not screenshot:
-                continue
-            valid_steps.append((obs, action, screenshot))
+                raise RolloutLossError(
+                    f"Rollout {rollout.task_id!r} step {step_index} is missing "
+                    "its screenshot"
+                )
+            valid_steps.append((step_index, obs, action, screenshot))
 
         if not valid_steps:
-            return 0.0
+            raise RolloutLossError(
+                f"Rollout {rollout.task_id!r} has no trainable observation/action steps"
+            )
 
         num_steps = len(valid_steps)
 
-        for _obs, action, screenshot in valid_steps:
+        for step_index, _obs, action, screenshot in valid_steps:
             try:
                 image = Image.open(io.BytesIO(screenshot)).convert("RGB")
-            except Exception:
-                continue
+            except Exception as exc:
+                raise RolloutLossError(
+                    f"Rollout {rollout.task_id!r} step {step_index} has an "
+                    "unreadable screenshot"
+                ) from exc
 
             messages = _build_agent_messages(instruction, include_image=True)
 
@@ -736,7 +913,9 @@ def _compute_rollout_loss(
             )["input_ids"]
             action_len = action_ids.shape[1]
             if action_len <= 0:
-                continue
+                raise RolloutLossError(
+                    f"Rollout {rollout.task_id!r} step {step_index} has no action tokens"
+                )
 
             full_ids = torch.cat([prompt_ids, action_ids.to(prompt_ids.device)], dim=1)
             full_inputs = dict(prompt_inputs)
@@ -767,6 +946,11 @@ def _compute_rollout_loss(
                 step_log_prob.detach().unsqueeze(0),
                 adv_tensor.unsqueeze(0),
             )
+            if not bool(torch.isfinite(step_loss).all()):
+                raise RolloutLossError(
+                    f"Rollout {rollout.task_id!r} step {step_index} produced "
+                    "a non-finite loss"
+                )
 
             # Per-step backward to avoid OOM
             scaled = step_loss * loss_scale / num_steps
diff --git a/pyproject.toml b/pyproject.toml
index 4202be7..2be13ea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -82,7 +82,9 @@ dev = [
 ]
 # Benchmark evaluation (depends on openadapt-evals)
 benchmarks = [
-    "openadapt-evals>=0.1.1",
+    # Keep ML on the tested Evals 0.90 contract lane. Evals is still pre-1.0,
+    # so the next minor release is an explicit compatibility review boundary.
+    "openadapt-evals>=0.90,<0.91",
 ]
 
 [project.urls]
diff --git a/scripts/__init__.py b/scripts/__init__.py
new file mode 100644
index 0000000..92bd9c3
--- /dev/null
+++ b/scripts/__init__.py
@@ -0,0 +1 @@
+"""Repository-local validation and operations scripts."""
diff --git a/scripts/validate_grpo_waa.py b/scripts/validate_grpo_waa.py
index 21c5c8c..fd5b91c 100644
--- a/scripts/validate_grpo_waa.py
+++ b/scripts/validate_grpo_waa.py
@@ -20,9 +20,13 @@
 from __future__ import annotations
 
 import argparse
+import json
 import logging
+import math
 import sys
 import time
+from pathlib import Path
+from urllib.parse import urlsplit
 
 logging.basicConfig(
     level=logging.INFO,
@@ -31,6 +35,170 @@
 logger = logging.getLogger("validate_grpo_waa")
 
 
+def _adapter_weight_issue(weight_path: Path) -> str | None:
+    """Return why an adapter weight file is not a readable tensor state."""
+    try:
+        import torch
+
+        def tensor_issue(tensor: torch.Tensor) -> str | None:
+            if tensor.layout != torch.strided:
+                return "adapter weights contain an unsupported tensor layout"
+            if tensor.numel() == 0:
+                return "adapter weights contain an empty tensor"
+            if (
+                tensor.is_floating_point() or tensor.is_complex()
+            ) and not torch.isfinite(tensor).all():
+                return "adapter weights contain a non-finite tensor"
+            return None
+
+        if weight_path.suffix == ".safetensors":
+            from safetensors import safe_open
+
+            with safe_open(str(weight_path), framework="pt", device="cpu") as tensors:
+                keys = list(tensors.keys())
+                if not keys:
+                    return "adapter weights contain no tensors"
+                for key in keys:
+                    if (issue := tensor_issue(tensors.get_tensor(key))) is not None:
+                        return issue
+            return None
+
+        state = torch.load(weight_path, map_location="cpu", weights_only=True)
+        if not isinstance(state, dict) or not state:
+            return "adapter weights must contain a non-empty tensor state"
+        if any(not isinstance(value, torch.Tensor) for value in state.values()):
+            return "adapter weights contain a non-tensor value"
+        for value in state.values():
+            if (issue := tensor_issue(value)) is not None:
+                return issue
+    except Exception as exc:
+        return f"adapter weights are unreadable: {type(exc).__name__}"
+    return None
+
+
+def _checkpoint_issue(checkpoint_dir: Path) -> str | None:
+    """Return why a PEFT adapter checkpoint cannot be loaded."""
+    if not checkpoint_dir.is_dir():
+        return "checkpoint directory is missing"
+
+    config_path = checkpoint_dir / "adapter_config.json"
+    if not config_path.is_file() or config_path.stat().st_size == 0:
+        return "adapter_config.json is missing or empty"
+    try:
+        config = json.loads(config_path.read_text(encoding="utf-8"))
+    except (OSError, UnicodeDecodeError, json.JSONDecodeError):
+        return "adapter_config.json is not valid JSON"
+    if not isinstance(config, dict) or not config:
+        return "adapter_config.json must contain a non-empty JSON object"
+    try:
+        from peft import PeftConfig
+
+        PeftConfig.from_pretrained(str(checkpoint_dir))
+    except Exception as exc:
+        return f"adapter_config.json is not valid PEFT metadata: {type(exc).__name__}"
+
+    weight_paths = [
+        checkpoint_dir / "adapter_model.safetensors",
+        checkpoint_dir / "adapter_model.bin",
+    ]
+    present_weights = [
+        path for path in weight_paths if path.is_file() and path.stat().st_size > 0
+    ]
+    if not present_weights:
+        return "adapter weights are missing or empty"
+    for weight_path in present_weights:
+        if (issue := _adapter_weight_issue(weight_path)) is not None:
+            return issue
+    return None
+
+
+def _missing_checkpoints(output_dir: str, steps: list[int]) -> list[int]:
+    """Return training steps whose adapter checkpoint is incomplete."""
+
+    root = Path(output_dir)
+    return [
+        step for step in steps if _checkpoint_issue(root / f"step_{step}") is not None
+    ]
+
+
+def _evaluator_candidates(server_url: str, evaluate_url: str | None) -> list[str]:
+    """Return evaluator URL candidates in deployment-preference order."""
+    if evaluate_url:
+        return [evaluate_url.rstrip("/")]
+
+    parsed = urlsplit(server_url)
+    if not parsed.scheme or not parsed.hostname:
+        return [server_url.rstrip("/")]
+    host = parsed.hostname
+    if ":" in host:
+        host = f"[{host}]"
+    candidates = [
+        f"{parsed.scheme}://{host}:5050",
+        f"{parsed.scheme}://{host}:5051",
+        server_url.rstrip("/"),
+    ]
+    return list(dict.fromkeys(candidates))
+
+
+def _detect_evaluator_url(
+    server_url: str,
+    evaluate_url: str | None,
+    *,
+    timeout: float = 10,
+) -> str | None:
+    """Find the endpoint that identifies itself as the evaluator service."""
+    import requests
+
+    for candidate in _evaluator_candidates(server_url, evaluate_url):
+        try:
+            response = requests.get(f"{candidate}/probe", timeout=timeout)
+            payload = response.json() if response.status_code == 200 else None
+        except Exception:
+            continue
+        if (
+            isinstance(payload, dict)
+            and payload.get("status") == "ok"
+            and payload.get("service") == "evaluate_server"
+        ):
+            return candidate
+    return None
+
+
+def _has_measured_training_update(
+    trainer: object, expected_steps: set[int] | None = None
+) -> bool:
+    """Return whether each requested step has one measured optimizer update."""
+    measured_steps: set[int] = set()
+    found_update = False
+    for metrics in getattr(trainer, "training_metrics", ()):
+        if not isinstance(metrics, dict) or metrics.get("skipped") is not False:
+            continue
+        numeric_fields = ("reward_mean", "loss", "gradient_norm")
+        if any(
+            isinstance(metrics.get(field), bool)
+            or not isinstance(metrics.get(field), (int, float))
+            or not math.isfinite(metrics[field])
+            for field in numeric_fields
+        ):
+            continue
+        gradient_terms = metrics.get("num_gradient_terms")
+        if (
+            isinstance(gradient_terms, bool)
+            or not isinstance(gradient_terms, int)
+            or gradient_terms <= 0
+            or metrics["gradient_norm"] <= 0.0
+            or metrics.get("optimizer_step_applied") is not True
+        ):
+            continue
+        found_update = True
+        step = metrics.get("step")
+        if isinstance(step, int) and not isinstance(step, bool) and step >= 0:
+            measured_steps.add(step)
+    if expected_steps is None:
+        return found_update
+    return measured_steps == expected_steps
+
+
 def phase1_connectivity(server_url: str, evaluate_url: str | None) -> bool:
     """Phase 1: Check WAA server connectivity."""
     import requests
@@ -51,13 +219,13 @@ def phase1_connectivity(server_url: str, evaluate_url: str | None) -> bool:
         logger.error("  /screenshot unreachable: %s", e)
         return False
 
-    # Check evaluate endpoint
-    eval_base = evaluate_url or server_url
-    try:
-        r = requests.get(f"{eval_base}/probe", timeout=10)
-        logger.info("  /probe status=%d", r.status_code)
-    except Exception as e:
-        logger.warning("  /probe unreachable (non-fatal): %s", e)
+    evaluator_url = _detect_evaluator_url(server_url, evaluate_url)
+    if evaluator_url is None:
+        logger.error(
+            "  no evaluator returned the canonical evaluate_server probe response"
+        )
+        return False
+    logger.info("  evaluator ready at %s", evaluator_url)
 
     logger.info("Phase 1 PASSED")
     return True
@@ -76,8 +244,15 @@ def phase2_single_rollout(
     else:
         from openadapt_evals.adapters.waa.live import WAALiveAdapter, WAALiveConfig
 
+        resolved_evaluate_url = _detect_evaluator_url(server_url, evaluate_url)
+        if resolved_evaluate_url is None:
+            logger.error("  No canonical evaluator endpoint is available")
+            return False
         adapter = WAALiveAdapter(
-            WAALiveConfig(server_url=server_url, evaluate_url=evaluate_url)
+            WAALiveConfig(
+                server_url=server_url,
+                evaluate_url=resolved_evaluate_url,
+            )
         )
 
     from openadapt_evals.adapters.base import BenchmarkAction
@@ -92,11 +267,30 @@ def phase2_single_rollout(
         return False
     logger.info("  Reset OK, screenshot=%d bytes", len(obs.screenshot))
 
-    # Take one action
-    action = BenchmarkAction(type="click", x=500, y=400)
+    # Advance through a no-op so validation cannot click an unknown live target.
+    action = BenchmarkAction(type="wait")
     step = env.step(action)
     logger.info("  Step OK: reward=%.2f, done=%s", step.reward, step.done)
 
+    try:
+        measured_score = env.evaluate()
+    except Exception as exc:
+        logger.error("  Evaluator failed: %s", exc)
+        if hasattr(adapter, "close"):
+            adapter.close()
+        return False
+    if (
+        isinstance(measured_score, bool)
+        or not isinstance(measured_score, (int, float))
+        or not math.isfinite(measured_score)
+        or not 0.0 <= measured_score <= 1.0
+    ):
+        logger.error("  Evaluator returned invalid score: %r", measured_score)
+        if hasattr(adapter, "close"):
+            adapter.close()
+        return False
+    logger.info("  Evaluator measured score=%.2f", measured_score)
+
     # Check screen size
     logger.info("  Screen size: %s", env.screen_size)
 
@@ -193,11 +387,18 @@ def phase4_single_training_step(
     from openadapt_ml.training.grpo.config import GRPOConfig
     from openadapt_ml.training.grpo.trainer import GRPOTrainer
 
+    resolved_evaluate_url = evaluate_url
+    if not mock:
+        resolved_evaluate_url = _detect_evaluator_url(server_url, evaluate_url)
+        if resolved_evaluate_url is None:
+            logger.error("  No canonical evaluator endpoint is available")
+            return False
+
     with tempfile.TemporaryDirectory() as tmpdir:
         config = GRPOConfig(
             model_name=model_name,
             server_url=server_url,
-            evaluate_url=evaluate_url,
+            evaluate_url=resolved_evaluate_url,
             task_ids=[task_id],
             lora_checkpoint=lora_checkpoint,
             num_rollouts_per_step=2,  # Small group for validation
@@ -221,12 +422,14 @@ def phase4_single_training_step(
         logger.info("  Training step completed in %.1fs", elapsed)
         logger.info("  Checkpoint: %s", checkpoint_path)
 
-        # Verify checkpoint exists
-        from pathlib import Path
+        if not _has_measured_training_update(trainer, expected_steps={0}):
+            logger.error("  Training produced no finite non-skipped optimizer update")
+            return False
 
         ckpt = Path(checkpoint_path)
-        if not ckpt.exists():
-            logger.error("  Checkpoint directory missing!")
+        issue = _checkpoint_issue(ckpt)
+        if issue is not None:
+            logger.error("  Checkpoint is incomplete: %s", issue)
             return False
 
         adapter_files = list(ckpt.glob("adapter_*"))
@@ -248,16 +451,22 @@ def phase5_multi_step_training(
     logger.info("=== Phase 5: Multi-Step Training ===")
 
     import tempfile
-    from pathlib import Path
 
     from openadapt_ml.training.grpo.config import GRPOConfig
     from openadapt_ml.training.grpo.trainer import GRPOTrainer
 
+    resolved_evaluate_url = evaluate_url
+    if not mock:
+        resolved_evaluate_url = _detect_evaluator_url(server_url, evaluate_url)
+        if resolved_evaluate_url is None:
+            logger.error("  No canonical evaluator endpoint is available")
+            return False
+
     with tempfile.TemporaryDirectory() as tmpdir:
         config = GRPOConfig(
             model_name=model_name,
             server_url=server_url,
-            evaluate_url=evaluate_url,
+            evaluate_url=resolved_evaluate_url,
             task_ids=[task_id],
             lora_checkpoint=lora_checkpoint,
             num_rollouts_per_step=2,
@@ -274,13 +483,23 @@ def phase5_multi_step_training(
 
         logger.info("  3 training steps completed in %.1fs", elapsed)
 
+        if not _has_measured_training_update(trainer, expected_steps={0, 1, 2}):
+            logger.error("  Training did not update each of steps 0, 1, and 2")
+            return False
+
         # Verify all checkpoints
         for step in [1, 2, 3]:
             ckpt = Path(tmpdir) / f"step_{step}"
-            if ckpt.exists():
+            issue = _checkpoint_issue(ckpt)
+            if issue is None:
                 logger.info("  step_%d checkpoint: OK", step)
             else:
-                logger.warning("  step_%d checkpoint: MISSING", step)
+                logger.error("  step_%d checkpoint: %s", step, issue)
+
+        missing = _missing_checkpoints(tmpdir, [1, 2, 3])
+        if missing:
+            logger.error("Phase 5 FAILED: missing checkpoints %s", missing)
+            return False
 
     logger.info("Phase 5 PASSED")
     return True
diff --git a/tests/test_failure_is_not_success.py b/tests/test_failure_is_not_success.py
index 90474c9..7792fa2 100644
--- a/tests/test_failure_is_not_success.py
+++ b/tests/test_failure_is_not_success.py
@@ -13,13 +13,24 @@
 from __future__ import annotations
 
 import ast
+import io
 import sys
 from dataclasses import fields
 from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import MagicMock
 
 import pytest
 
 
+def _png_bytes() -> bytes:
+    from PIL import Image
+
+    buffer = io.BytesIO()
+    Image.new("RGB", (2, 2), "white").save(buffer, format="PNG")
+    return buffer.getvalue()
+
+
 def _error_type(module_path: str, name: str) -> type[BaseException]:
     """Resolve an exception type by name, falling back to Exception.
 
@@ -76,9 +87,8 @@ def test_grpo_config_declares_task_dir_exactly_once():
     """
     source = (PACKAGE_ROOT / "training" / "grpo" / "config.py").read_text()
     duplicates = _duplicate_annotated_fields(ast.parse(source))
-    assert duplicates == [], (
-        "GRPOConfig re-declares dataclass field(s): "
-        + ", ".join(f"{cls}.{name} at line {line}" for cls, name, line in duplicates)
+    assert duplicates == [], "GRPOConfig re-declares dataclass field(s): " + ", ".join(
+        f"{cls}.{name} at line {line}" for cls, name, line in duplicates
     )
 
 
@@ -106,6 +116,74 @@ def test_grpo_config_task_dir_keeps_its_documented_meaning():
     )
 
 
+def test_grpo_loss_refuses_missing_or_unreadable_rollout_evidence():
+    """Missing screenshots must not become a successful zero-loss update."""
+    from openadapt_ml.training.grpo.config import GRPOConfig
+    from openadapt_ml.training.grpo.rollout_collector import Rollout
+    from openadapt_ml.training.grpo.trainer import GRPOTrainer, RolloutLossError
+
+    trainer = object.__new__(GRPOTrainer)
+    trainer._config = GRPOConfig()
+    trainer._collector = None
+    trainer._model = MagicMock()
+    parameter = MagicMock(device="cpu")
+    trainer._model.parameters.return_value = iter([parameter])
+
+    with pytest.raises(RolloutLossError, match="no trainable"):
+        trainer._compute_rollout_loss(Rollout(task_id="empty"), advantage=1.0)
+
+    trainer._model.parameters.return_value = iter([parameter])
+    broken = Rollout(
+        task_id="broken",
+        steps=[
+            SimpleNamespace(
+                observation=SimpleNamespace(screenshot=b"not an image"),
+                action=SimpleNamespace(type="click", x=1, y=1),
+            )
+        ],
+    )
+    with pytest.raises(RolloutLossError, match="unreadable screenshot"):
+        trainer._compute_rollout_loss(broken, advantage=1.0)
+
+
+def test_grpo_collector_does_not_score_an_empty_rollout_as_zero():
+    from openadapt_ml.training.grpo.config import GRPOConfig
+    from openadapt_ml.training.grpo.rollout_collector import (
+        GRPORolloutCollector,
+        RolloutCollectionError,
+    )
+
+    collector = object.__new__(GRPORolloutCollector)
+    collector._config = GRPOConfig(
+        task_ids=["task-1"],
+        num_rollouts_per_step=1,
+    )
+    collector._task_configs = {}
+    collector._env = MagicMock()
+    collector._env.collect_rollout.return_value = []
+
+    with pytest.raises(RolloutCollectionError, match="no rollout steps"):
+        collector.collect_group(agent_fn=MagicMock(), task_id="task-1")
+
+
+@pytest.mark.parametrize("score", [float("nan"), float("inf"), -0.1, 1.1])
+def test_grpo_collector_rejects_invalid_terminal_score(score):
+    from openadapt_ml.training.grpo.config import GRPOConfig
+    from openadapt_ml.training.grpo.rollout_collector import (
+        GRPORolloutCollector,
+        RolloutCollectionError,
+    )
+
+    collector = object.__new__(GRPORolloutCollector)
+    collector._config = GRPOConfig(task_ids=["task-1"], num_rollouts_per_step=1)
+    collector._task_configs = {}
+    collector._env = MagicMock()
+    collector._env.collect_rollout.return_value = [SimpleNamespace(reward=score)]
+
+    with pytest.raises(RolloutCollectionError, match="invalid terminal score"):
+        collector.collect_group(agent_fn=MagicMock(), task_id="task-1")
+
+
 # ---------------------------------------------------------------------------
 # 2. GRPO milestone reward: "could not evaluate" must not be reward 0.0
 # ---------------------------------------------------------------------------
@@ -145,7 +223,7 @@ def test_milestone_reward_raises_when_evals_package_is_missing(monkeypatch):
     config = _TaskConfig([_Milestone()])
 
     with pytest.raises(MilestoneEvaluationError, match="openadapt-evals"):
-        evaluate_milestones_screenshot(config, b"png-bytes")
+        evaluate_milestones_screenshot(config, _png_bytes())
 
 
 def test_milestone_reward_raises_when_the_vlm_judge_fails(monkeypatch):
@@ -173,7 +251,7 @@ def _boom(*args, **kwargs):
     config = _TaskConfig([_Milestone("m1"), _Milestone("m2")])
 
     with pytest.raises(MilestoneEvaluationError, match="not a failed milestone"):
-        evaluate_milestones_screenshot(config, b"png-bytes")
+        evaluate_milestones_screenshot(config, _png_bytes())
 
 
 def test_milestone_reward_raises_on_a_milestone_with_no_description(monkeypatch):
@@ -199,7 +277,7 @@ def test_milestone_reward_raises_on_a_milestone_with_no_description(monkeypatch)
     )
 
     with pytest.raises(MilestoneEvaluationError, match="no description"):
-        evaluate_milestones_screenshot(config, b"png-bytes")
+        evaluate_milestones_screenshot(config, _png_bytes())
 
 
 def test_milestone_reward_raises_when_there_is_nothing_locally_evaluable():
@@ -211,11 +289,58 @@ def test_milestone_reward_raises_when_there_is_nothing_locally_evaluable():
     )
 
     with pytest.raises(MilestoneEvaluationError, match="no milestones"):
-        evaluate_milestones_screenshot(_TaskConfig([]), b"png-bytes")
+        evaluate_milestones_screenshot(_TaskConfig([]), _png_bytes())
 
     server_only = _TaskConfig([_Milestone("m", _Check(check="http"))])
     with pytest.raises(MilestoneEvaluationError, match="none of type 'screenshot'"):
-        evaluate_milestones_screenshot(server_only, b"png-bytes")
+        evaluate_milestones_screenshot(server_only, _png_bytes())
+
+
+def test_milestone_reward_rejects_empty_screenshot():
+    from openadapt_ml.training.grpo.reward import (
+        MilestoneEvaluationError,
+        evaluate_milestones_screenshot,
+    )
+
+    with pytest.raises(MilestoneEvaluationError, match="non-empty screenshot"):
+        evaluate_milestones_screenshot(_TaskConfig([_Milestone()]), b"")
+
+
+def test_milestone_reward_rejects_undecodable_screenshot():
+    from openadapt_ml.training.grpo.reward import (
+        MilestoneEvaluationError,
+        evaluate_milestones_screenshot,
+    )
+
+    with pytest.raises(MilestoneEvaluationError, match="decodable image"):
+        evaluate_milestones_screenshot(_TaskConfig([_Milestone()]), b"not-an-image")
+
+
+@pytest.mark.parametrize(
+    "judge_result, message",
+    [
+        ((1, 0.9), "non-boolean success"),
+        ((True, float("nan")), "invalid confidence"),
+        ((False, 1.1), "invalid confidence"),
+    ],
+)
+def test_milestone_reward_rejects_invalid_judge_evidence(
+    monkeypatch, judge_result, message
+):
+    import types
+
+    from openadapt_ml.training.grpo.reward import (
+        MilestoneEvaluationError,
+        evaluate_milestones_screenshot,
+    )
+
+    fake = types.ModuleType("openadapt_evals.vlm_evaluator")
+    fake.vlm_judge = lambda *args, **kwargs: judge_result
+    monkeypatch.setitem(sys.modules, "openadapt_evals", types.ModuleType("oe"))
+    monkeypatch.setitem(sys.modules, "openadapt_evals.vlm_evaluator", fake)
+
+    with pytest.raises(MilestoneEvaluationError, match=message):
+        evaluate_milestones_screenshot(_TaskConfig([_Milestone()]), _png_bytes())
 
 
 def test_milestone_reward_still_returns_a_real_score(monkeypatch):
@@ -236,7 +361,7 @@ def _judge(screenshot, description, **kwargs):
     monkeypatch.setitem(sys.modules, "openadapt_evals.vlm_evaluator", fake)
 
     config = _TaskConfig([_Milestone("a"), _Milestone("b")])
-    assert evaluate_milestones_screenshot(config, b"png-bytes") == 0.5
+    assert evaluate_milestones_screenshot(config, _png_bytes()) == 0.5
     assert len(calls) == 2
 
 
@@ -293,7 +418,7 @@ def generate_content(self, *args, **kwargs):
 def test_gemini_grounder_still_returns_an_empty_list_for_a_real_no_match(
     monkeypatch,
 ):
-    """"I looked and matched nothing" must keep returning []."""
+    """ "I looked and matched nothing" must keep returning []."""
     from PIL import Image
 
     from openadapt_ml.grounding.detector import GeminiGrounder
diff --git a/tests/test_grpo.py b/tests/test_grpo.py
index e8cc48f..c1bdd4e 100644
--- a/tests/test_grpo.py
+++ b/tests/test_grpo.py
@@ -94,6 +94,22 @@ def test_binary_reward_custom_threshold():
     assert binary_task_success(0.29, threshold=0.3) == 0.0
 
 
+@pytest.mark.parametrize("score", [True, float("nan"), float("inf"), -0.1, 1.1])
+def test_binary_reward_rejects_invalid_score(score):
+    from openadapt_ml.training.grpo.reward import binary_task_success
+
+    with pytest.raises(ValueError, match="score"):
+        binary_task_success(score)
+
+
+@pytest.mark.parametrize("threshold", [False, float("nan"), -0.1, 1.1])
+def test_binary_reward_rejects_invalid_threshold(threshold):
+    from openadapt_ml.training.grpo.reward import binary_task_success
+
+    with pytest.raises(ValueError, match="threshold"):
+        binary_task_success(0.5, threshold=threshold)
+
+
 def test_group_advantages_mixed():
     """Mixed rewards produce positive/negative advantages."""
     from openadapt_ml.training.grpo.reward import compute_group_advantages
@@ -147,6 +163,16 @@ def test_group_advantages_normalized():
     assert abs(mean_adv) < 1e-6  # Mean should be ~0
 
 
+@pytest.mark.parametrize(
+    "rewards", [[0.0, True], [0.0, float("nan")], [-0.1, 1.0], [0.0, 1.1]]
+)
+def test_group_advantages_reject_invalid_reward(rewards):
+    from openadapt_ml.training.grpo.reward import compute_group_advantages
+
+    with pytest.raises(ValueError, match=r"rewards\[1\]|rewards\[0\]"):
+        compute_group_advantages(rewards)
+
+
 # ---------------------------------------------------------------------------
 # Rollout dataclass tests
 # ---------------------------------------------------------------------------
@@ -396,22 +422,42 @@ def test_parse_click_custom_screen_size():
     assert action.y == 400
 
 
-def test_parse_click_clamps_negative():
-    """_parse_vlm_output_to_action clamps negative coordinates to 0."""
+def test_parse_click_inclusive_normalized_edge_stays_inside_viewport():
     from openadapt_ml.training.grpo.trainer import _parse_vlm_output_to_action
 
-    action = _parse_vlm_output_to_action("CLICK(x=-0.1, y=-0.2)")
-    assert action.x == 0
-    assert action.y == 0
+    action = _parse_vlm_output_to_action(
+        "CLICK(x=1.0, y=1.0)", screen_size=(1920, 1080)
+    )
+    assert (action.x, action.y) == (1919, 1079)
 
 
-def test_parse_click_clamps_above_one():
-    """_parse_vlm_output_to_action clamps coordinates above 1.0."""
-    from openadapt_ml.training.grpo.trainer import _parse_vlm_output_to_action
+@pytest.mark.parametrize("coordinates", ["-0.1, y=-0.2", "1.5, y=2.0"])
+def test_parse_click_rejects_out_of_range_coordinates(coordinates):
+    from openadapt_ml.training.grpo.trainer import (
+        ActionParseError,
+        _parse_vlm_output_to_action,
+    )
+
+    with pytest.raises(ActionParseError, match="fractions"):
+        _parse_vlm_output_to_action(f"CLICK(x={coordinates})")
+
+
+@pytest.mark.parametrize(
+    "payload",
+    [
+        '{"action_type":"click","coordinate":[0.5,400]}',
+        '{"action_type":"click","coordinate":["NaN",0.5]}',
+        '{"action_type":"click","coordinate":[1920,1080]}',
+    ],
+)
+def test_parse_json_click_rejects_unsafe_coordinate_units(payload):
+    from openadapt_ml.training.grpo.trainer import (
+        ActionParseError,
+        _parse_vlm_output_to_action,
+    )
 
-    action = _parse_vlm_output_to_action("CLICK(x=1.5, y=2.0)")
-    assert action.x == 1920
-    assert action.y == 1080
+    with pytest.raises(ActionParseError):
+        _parse_vlm_output_to_action(payload)
 
 
 def test_parse_type_action():
@@ -432,6 +478,20 @@ def test_parse_type_action_single_quotes():
     assert action.text == "hello world"
 
 
+def test_parse_json_type_requires_explicit_text():
+    from openadapt_ml.training.grpo.trainer import (
+        ActionParseError,
+        _parse_vlm_output_to_action,
+    )
+
+    with pytest.raises(ActionParseError, match="explicit string text"):
+        _parse_vlm_output_to_action('{"action_type":"type"}')
+
+    action = _parse_vlm_output_to_action('{"action_type":"type","text":""}')
+    assert action.type == "type"
+    assert action.text == ""
+
+
 def test_parse_wait_action():
     """_parse_vlm_output_to_action correctly parses WAIT actions."""
     from openadapt_ml.training.grpo.trainer import _parse_vlm_output_to_action
@@ -448,12 +508,305 @@ def test_parse_done_action():
     assert action.type == "done"
 
 
-def test_parse_fallback_to_done():
-    """_parse_vlm_output_to_action falls back to DONE for unparseable output."""
-    from openadapt_ml.training.grpo.trainer import _parse_vlm_output_to_action
+def test_unparseable_output_is_not_task_completion():
+    from openadapt_ml.training.grpo.trainer import (
+        ActionParseError,
+        _parse_vlm_output_to_action,
+    )
 
-    action = _parse_vlm_output_to_action("I don't know what to do")
-    assert action.type == "done"
+    with pytest.raises(ActionParseError):
+        _parse_vlm_output_to_action("I don't know what to do")
+
+
+def test_parser_requires_one_complete_action_envelope():
+    from openadapt_ml.training.grpo.trainer import (
+        ActionParseError,
+        _parse_vlm_output_to_action,
+    )
+
+    valid = _parse_vlm_output_to_action(
+        "Thought: use the visible target\nAction: CLICK(x=0.5, y=0.25)"
+    )
+    assert valid.type == "click"
+
+    invalid_outputs = [
+        "CLICK(x=0.5, y=0.25)\nDONE()",
+        "Action: WAIT()\nAction: DONE()",
+        '{"action_type":"wait"} {"action_type":"done"}',
+        "Action: WAIT()\nthen CLICK(x=0.5, y=0.5)",
+    ]
+    for output in invalid_outputs:
+        with pytest.raises(ActionParseError):
+            _parse_vlm_output_to_action(output)
+
+
+@pytest.mark.parametrize(
+    "output",
+    [
+        '{"action_type":"done","action_type":"click","coordinate":[0.5,0.5]}',
+        '{"action_type":"done","coordinate":[0.5,0.5]}',
+        '{"action_type":"click","coordinate":[0.5,0.5],"coords":[0.5,0.5]}',
+        '{"action_type":"click","coordinate":[0.5,0.5],"coordinate_mode":"normalized","coordinate_space":"normalized"}',
+        '{"action_type":"type","text":"hello","coordinate":[0.5,0.5]}',
+        '{"action_type":"wait","text":"ignored"}',
+    ],
+)
+def test_json_parser_rejects_duplicate_competing_or_extra_fields(output):
+    from openadapt_ml.training.grpo.trainer import (
+        ActionParseError,
+        _parse_vlm_output_to_action,
+    )
+
+    with pytest.raises(ActionParseError):
+        _parse_vlm_output_to_action(output)
+
+
+def test_incomplete_checkpoint_fails_validation(tmp_path):
+    import torch
+    from safetensors.torch import save_file
+
+    from scripts.validate_grpo_waa import _missing_checkpoints
+
+    checkpoint = tmp_path / "step_1"
+    checkpoint.mkdir()
+    assert _missing_checkpoints(str(tmp_path), [1, 2, 3]) == [1, 2, 3]
+
+    (checkpoint / "adapter_config.json").write_text("not json")
+    (checkpoint / "adapter_model.safetensors").write_bytes(b"weights")
+    assert _missing_checkpoints(str(tmp_path), [1]) == [1]
+
+    (checkpoint / "adapter_config.json").write_text("{}")
+    assert _missing_checkpoints(str(tmp_path), [1]) == [1]
+
+    (checkpoint / "adapter_config.json").write_text('{"peft_type":"LORA"}')
+    (checkpoint / "adapter_model.safetensors").write_bytes(b"")
+    assert _missing_checkpoints(str(tmp_path), [1]) == [1]
+
+    (checkpoint / "adapter_model.safetensors").write_bytes(b"arbitrary bytes")
+    assert _missing_checkpoints(str(tmp_path), [1]) == [1]
+
+    save_file(
+        {"base_model.model.layer.lora_A.weight": torch.tensor([float("nan")])},
+        checkpoint / "adapter_model.safetensors",
+    )
+    assert _missing_checkpoints(str(tmp_path), [1]) == [1]
+
+    save_file(
+        {"base_model.model.layer.lora_A.weight": torch.ones(1, 1)},
+        checkpoint / "adapter_model.safetensors",
+    )
+    assert _missing_checkpoints(str(tmp_path), [1, 2, 3]) == [2, 3]
+
+    (checkpoint / "adapter_model.safetensors").unlink()
+    (checkpoint / "adapter_model.bin").write_bytes(b"arbitrary bytes")
+    assert _missing_checkpoints(str(tmp_path), [1]) == [1]
+
+    torch.save(
+        {"base_model.model.layer.lora_A.weight": torch.tensor([float("inf")])},
+        checkpoint / "adapter_model.bin",
+    )
+    assert _missing_checkpoints(str(tmp_path), [1]) == [1]
+
+    torch.save(
+        {"base_model.model.layer.lora_A.weight": torch.ones(1, 1)},
+        checkpoint / "adapter_model.bin",
+    )
+    assert _missing_checkpoints(str(tmp_path), [1, 2, 3]) == [2, 3]
+
+
+def test_training_step_rejects_empty_rollout_group():
+    from openadapt_ml.training.grpo.config import GRPOConfig
+    from openadapt_ml.training.grpo.trainer import GRPOTrainer, RolloutLossError
+
+    trainer = GRPOTrainer(GRPOConfig())
+    with pytest.raises(RolloutLossError, match="empty rollout group"):
+        trainer._training_step([])
+
+
+@pytest.mark.parametrize(
+    "probe_status, probe_body",
+    [(503, b"unavailable"), (200, b"")],
+)
+def test_connectivity_requires_live_evaluator_probe(
+    monkeypatch, probe_status, probe_body
+):
+    from types import SimpleNamespace
+
+    from scripts.validate_grpo_waa import phase1_connectivity
+
+    responses = iter(
+        [
+            SimpleNamespace(status_code=200, content=b"screenshot" * 20),
+            SimpleNamespace(status_code=probe_status, content=probe_body),
+        ]
+    )
+    monkeypatch.setattr("requests.get", lambda *args, **kwargs: next(responses))
+
+    assert phase1_connectivity("http://waa", "http://evaluator") is False
+
+
+def test_evaluator_auto_detection_requires_canonical_service(monkeypatch):
+    from scripts.validate_grpo_waa import _detect_evaluator_url
+
+    class Response:
+        status_code = 200
+
+        def __init__(self, payload):
+            self._payload = payload
+
+        def json(self):
+            return self._payload
+
+    responses = iter(
+        [
+            Response({"status": "ok", "service": "waa"}),
+            Response({"status": "ok", "service": "evaluate_server"}),
+        ]
+    )
+    requested = []
+
+    def get(url, **kwargs):
+        requested.append(url)
+        return next(responses)
+
+    monkeypatch.setattr("requests.get", get)
+
+    assert (
+        _detect_evaluator_url("http://worker.example:5000", None)
+        == "http://worker.example:5051"
+    )
+    assert requested == [
+        "http://worker.example:5050/probe",
+        "http://worker.example:5051/probe",
+    ]
+
+
+@pytest.mark.parametrize(
+    "metrics, expected",
+    [
+        (
+            [
+                {
+                    "skipped": False,
+                    "reward_mean": 0.5,
+                    "loss": 0.2,
+                    "gradient_norm": 0.3,
+                    "num_gradient_terms": 2,
+                    "optimizer_step_applied": True,
+                }
+            ],
+            True,
+        ),
+        ([{"skipped": True}], False),
+        (
+            [
+                {
+                    "skipped": False,
+                    "reward_mean": 0.5,
+                    "loss": 0.2,
+                    "gradient_norm": 0.0,
+                    "num_gradient_terms": 2,
+                    "optimizer_step_applied": True,
+                }
+            ],
+            False,
+        ),
+    ],
+)
+def test_training_validation_requires_measured_optimizer_update(metrics, expected):
+    from types import SimpleNamespace
+
+    from scripts.validate_grpo_waa import _has_measured_training_update
+
+    assert (
+        _has_measured_training_update(SimpleNamespace(training_metrics=metrics))
+        is expected
+    )
+
+
+def test_training_validation_requires_an_update_for_each_expected_step():
+    from types import SimpleNamespace
+
+    from scripts.validate_grpo_waa import _has_measured_training_update
+
+    def metric(step, *, skipped=False):
+        return {
+            "step": step,
+            "skipped": skipped,
+            "reward_mean": 0.5,
+            "loss": 0.2,
+            "gradient_norm": 0.3,
+            "num_gradient_terms": 2,
+            "optimizer_step_applied": True,
+        }
+
+    trainer = SimpleNamespace(
+        training_metrics=[metric(0), metric(1, skipped=True), metric(2)]
+    )
+    assert not _has_measured_training_update(trainer, expected_steps={0, 1, 2})
+
+    trainer.training_metrics[1] = metric(1)
+    assert _has_measured_training_update(trainer, expected_steps={0, 1, 2})
+
+
+def test_single_rollout_requires_measured_evaluator(monkeypatch):
+    import sys
+    from types import ModuleType, SimpleNamespace
+
+    from scripts.validate_grpo_waa import phase2_single_rollout
+
+    class FakeAdapter:
+        def close(self):
+            pass
+
+    class FakeAction:
+        def __init__(self, **kwargs):
+            self.__dict__.update(kwargs)
+
+    class FakeResetConfig:
+        def __init__(self, task_id):
+            self.task_id = task_id
+
+    observed_actions = []
+
+    class FakeEnvironment:
+        screen_size = (1280, 720)
+
+        def __init__(self, adapter):
+            self.adapter = adapter
+
+        def reset(self, config):
+            return SimpleNamespace(screenshot=b"screen")
+
+        def step(self, action):
+            observed_actions.append(action)
+            return SimpleNamespace(reward=0.0, done=False)
+
+        def evaluate(self):
+            raise RuntimeError("evaluator offline")
+
+    modules = {
+        "openadapt_evals": ModuleType("openadapt_evals"),
+        "openadapt_evals.adapters": ModuleType("openadapt_evals.adapters"),
+        "openadapt_evals.adapters.waa": ModuleType("openadapt_evals.adapters.waa"),
+        "openadapt_evals.adapters.waa.mock": ModuleType(
+            "openadapt_evals.adapters.waa.mock"
+        ),
+        "openadapt_evals.adapters.base": ModuleType("openadapt_evals.adapters.base"),
+        "openadapt_evals.adapters.rl_env": ModuleType(
+            "openadapt_evals.adapters.rl_env"
+        ),
+    }
+    modules["openadapt_evals.adapters.waa.mock"].WAAMockAdapter = FakeAdapter
+    modules["openadapt_evals.adapters.base"].BenchmarkAction = FakeAction
+    modules["openadapt_evals.adapters.rl_env"].ResetConfig = FakeResetConfig
+    modules["openadapt_evals.adapters.rl_env"].RLEnvironment = FakeEnvironment
+    for name, module in modules.items():
+        monkeypatch.setitem(sys.modules, name, module)
+
+    assert phase2_single_rollout("http://waa", None, "mock_notepad_001", True) is False
+    assert len(observed_actions) == 1
+    assert observed_actions[0].type == "wait"
 
 
 # ---------------------------------------------------------------------------
@@ -533,6 +886,18 @@ class FakeAction:
     assert result == 'TYPE(text="say \\"hi\\"")'
 
 
+def test_format_type_requires_explicit_text():
+    from types import SimpleNamespace
+
+    from openadapt_ml.training.grpo.trainer import (
+        ActionParseError,
+        _format_action_as_text,
+    )
+
+    with pytest.raises(ActionParseError, match="explicit string text"):
+        _format_action_as_text(SimpleNamespace(type="type", text=None))
+
+
 def test_format_wait_action():
     """_format_action_as_text formats WAIT."""
     from dataclasses import dataclass
@@ -567,11 +932,13 @@ class FakeAction:
     assert _format_action_as_text(FakeAction()) == "DONE()"
 
 
-def test_format_unknown_defaults_to_done():
-    """_format_action_as_text defaults to DONE for unknown types."""
+def test_format_unknown_action_is_not_task_completion():
     from dataclasses import dataclass
 
-    from openadapt_ml.training.grpo.trainer import _format_action_as_text
+    from openadapt_ml.training.grpo.trainer import (
+        ActionParseError,
+        _format_action_as_text,
+    )
 
     @dataclass
     class FakeAction:
@@ -581,7 +948,32 @@ class FakeAction:
         text: str = None
         key: str = None
 
-    assert _format_action_as_text(FakeAction()) == "DONE()"
+    with pytest.raises(ActionParseError, match="unsupported action"):
+        _format_action_as_text(FakeAction())
+
+
+def test_format_click_refuses_missing_coordinates():
+    from types import SimpleNamespace
+
+    from openadapt_ml.training.grpo.trainer import (
+        ActionParseError,
+        _format_action_as_text,
+    )
+
+    with pytest.raises(ActionParseError, match="without x and y"):
+        _format_action_as_text(SimpleNamespace(type="click", x=None, y=10))
+
+
+def test_format_click_refuses_coordinates_outside_viewport():
+    from types import SimpleNamespace
+
+    from openadapt_ml.training.grpo.trainer import (
+        ActionParseError,
+        _format_action_as_text,
+    )
+
+    with pytest.raises(ActionParseError, match="outside the screen"):
+        _format_action_as_text(SimpleNamespace(type="click", x=1920, y=10))
 
 
 def test_format_roundtrip_click():
diff --git a/uv.lock b/uv.lock
index 32c76e4..6ae4555 100644
--- a/uv.lock
+++ b/uv.lock
@@ -172,7 +172,7 @@ wheels = [
 
 [[package]]
 name = "alembic"
-version = "1.18.4"
+version = "1.18.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mako" },
@@ -180,9 +180,9 @@ dependencies = [
     { name = "tomli", marker = "python_full_version < '3.11'" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/94/13/8b084e0f2efb0275a1d534838844926f798bd766566b1375174e2448cd31/alembic-1.18.4.tar.gz", hash = "sha256:cb6e1fd84b6174ab8dbb2329f86d631ba9559dd78df550b57804d607672cedbc", size = 2056725, upload-time = "2026-02-10T16:00:47.195Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/1a/cc/ac0bed8e562e7407fe55c3ba85a4dce86e6dbd8730887bd1e406a6c5c18a/alembic-1.18.5.tar.gz", hash = "sha256:1554982221dd17e9a749b53902407578eb305e453f71999e8c7f0a48389fff8e", size = 2060480, upload-time = "2026-06-25T15:20:54.888Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d2/29/6533c317b74f707ea28f8d633734dbda2119bbadfc61b2f3640ba835d0f7/alembic-1.18.4-py3-none-any.whl", hash = "sha256:a5ed4adcf6d8a4cb575f3d759f071b03cd6e5c7618eb796cb52497be25bfe19a", size = 263893, upload-time = "2026-02-10T16:00:49.997Z" },
+    { url = "https://files.pythonhosted.org/packages/96/78/5fe6dc3a3a5b2f5a2a4faef8bfe336d5fa049a38884ab3172e0098160c01/alembic-1.18.5-py3-none-any.whl", hash = "sha256:06d8ba9d04558022f5395e9317de03d270f3dced49cee01f89fe7a13c26f14bc", size = 264664, upload-time = "2026-06-25T15:20:56.673Z" },
 ]
 
 [[package]]
@@ -205,7 +205,7 @@ wheels = [
 
 [[package]]
 name = "anthropic"
-version = "0.75.0"
+version = "0.120.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -217,9 +217,9 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/04/1f/08e95f4b7e2d35205ae5dcbb4ae97e7d477fc521c275c02609e2931ece2d/anthropic-0.75.0.tar.gz", hash = "sha256:e8607422f4ab616db2ea5baacc215dd5f028da99ce2f022e33c7c535b29f3dfb", size = 439565, upload-time = "2025-11-24T20:41:45.28Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/92/5c/3331da4fc009d448008a50c78d86cc929e8c937cd1442245ce3f80561c4e/anthropic-0.120.0.tar.gz", hash = "sha256:6ba6007dc9b00365b20f6101a6618f5196ac1ceef81512e4b5cc0e7436d4975d", size = 1008042, upload-time = "2026-07-24T16:32:52.384Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/60/1c/1cd02b7ae64302a6e06724bf80a96401d5313708651d277b1458504a1730/anthropic-0.75.0-py3-none-any.whl", hash = "sha256:ea8317271b6c15d80225a9f3c670152746e88805a7a61e14d4a374577164965b", size = 388164, upload-time = "2025-11-24T20:41:43.587Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/8a/8522bdf809e1f95f0d9c936540987a3f6afba01d2921a2bf488dedf836a8/anthropic-0.120.0-py3-none-any.whl", hash = "sha256:591bd531563ec7b63a1e138f5c11f14cb94edda99623b349c2ce2ece8e08b8a5", size = 1022602, upload-time = "2026-07-24T16:32:50.506Z" },
 ]
 
 [[package]]
@@ -266,94 +266,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" },
 ]
 
-[[package]]
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-version = "24.4.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "cryptography" },
-    { name = "hyperlink" },
-    { name = "setuptools" },
-    { name = "txaio" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/38/f2/8dffb3b709383ba5b47628b0cc4e43e8d12d59eecbddb62cfccac2e7cf6a/autobahn-24.4.2.tar.gz", hash = "sha256:a2d71ef1b0cf780b6d11f8b205fd2c7749765e65795f2ea7d823796642ee92c9", size = 482700, upload-time = "2024-08-02T09:26:48.241Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/13/ee/a6475f39ef6c6f41c33da6b193e0ffd2c6048f52e1698be6253c59301b72/autobahn-24.4.2-py2.py3-none-any.whl", hash = "sha256:c56a2abe7ac78abbfb778c02892d673a4de58fd004d088cd7ab297db25918e81", size = 666965, upload-time = "2024-08-02T09:26:44.274Z" },
-]
-
-[package.optional-dependencies]
-twisted = [
-    { name = "attrs" },
-    { name = "twisted" },
-    { name = "zope-interface" },
-]
-
-[[package]]
-name = "automat"
-version = "25.4.16"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e3/0f/d40bbe294bbf004d436a8bcbcfaadca8b5140d39ad0ad3d73d1a8ba15f14/automat-25.4.16.tar.gz", hash = "sha256:0017591a5477066e90d26b0e696ddc143baafd87b588cfac8100bc6be9634de0", size = 129977, upload-time = "2025-04-16T20:12:16.002Z" }
-wheels = [
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-]
-
-[[package]]
-name = "av"
-version = "16.0.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/15/c3/fd72a0315bc6c943ced1105aaac6e0ec1be57c70d8a616bd05acaa21ffee/av-16.0.1.tar.gz", hash = "sha256:dd2ce779fa0b5f5889a6d9e00fbbbc39f58e247e52d31044272648fe16ff1dbf", size = 3904030, upload-time = "2025-10-13T12:28:51.082Z" }
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+name = "comtypes"
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