diff --git a/backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in b/backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in
index 53af3e2499d..3d3a5ad2707 100644
--- a/backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in
+++ b/backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in
@@ -44,8 +44,8 @@ See [Partitioner API](arm-vgf-partitioner.md) for more information of the Partit
 
 ## Quantization
 
-The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_ptq.html)
-and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_qat.html).
+The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_ptq.html)
+and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_qat.html).
 
 Partial quantization is supported, allowing users to quantize only specific parts of the model while leaving others in floating-point.
 
diff --git a/docs/source/backends/arm-vgf/arm-vgf-overview.md b/docs/source/backends/arm-vgf/arm-vgf-overview.md
index 110c50af55c..2d5d8dc571b 100644
--- a/docs/source/backends/arm-vgf/arm-vgf-overview.md
+++ b/docs/source/backends/arm-vgf/arm-vgf-overview.md
@@ -112,8 +112,8 @@ See [Partitioner API](arm-vgf-partitioner.md) for more information of the Partit
 
 ## Quantization
 
-The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_ptq.html)
-and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_qat.html).
+The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_ptq.html)
+and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_qat.html).
 
 Partial quantization is supported, allowing users to quantize only specific parts of the model while leaving others in floating-point.