Update NSS model training gym LP

Author: annietllnd

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/1-introduction.md

@@ -12,11 +12,11 @@ Neural graphics is an intersection of graphics and machine learning. Rather than
 
 ## How does Arm support neural graphics?
 
-Arm enables neural graphics through the **Neural Graphics Development Kit**: a set of open-source tools that let developers train, evaluate, and deploy ML models for graphics workloads.
+Arm enables neural graphics through the [**Neural Graphics Development Kit**](https://developer.arm.com/mobile-graphics-and-gaming/neural-graphics): a set of open-source tools that let developers train, evaluate, and deploy ML models for graphics workloads.
 
 At its core are the ML Extensions for Vulkan, which bring native ML inference into the GPU pipeline using structured compute graphs. These extensions (`VK_ARM_tensors` and `VK_ARM_data_graph`) allow real-time upscaling and similar effects to run efficiently alongside rendering tasks.
 
-The neural graphics models can be developed using well-known ML frameworks like **PyTorch**, and exported to deployment using Arm's hardware-aware pipeline. The workflow converts .pt model weights to `.vgf` via the TOSA intermediate representation, making it possible to do tailored model development for you game use-case. This Learning Path focuses on **Neural Super Sampling (NSS)** as the use case for training, evaluating, and deploying neural models using a toolkit called the **Neural Graphics Model Gym**.
+The neural graphics models can be developed using well-known ML frameworks like **PyTorch**, and exported to deployment using Arm's hardware-aware pipeline. The workflow converts the model to `.vgf` via the TOSA intermediate representation, making it possible to do tailored model development for you game use-case. This Learning Path focuses on **Neural Super Sampling (NSS)** as the use case for training, evaluating, and deploying neural models using a toolkit called the [**Neural Graphics Model Gym**](https://github.com/arm/neural-graphics-model-gym). To learn more about NSS, you can check out the [resources on Hugging Face](https://huggingface.co/Arm/neural-super-sampling).
 
 Starting in 2026, Arm GPUs will feature dedicated neural accelerators, optimized for low-latency inference in graphics workloads. To help developers get started early, Arm provides the ML Emulation Layers for Vulkan that simulate future hardware behavior, so you can build and test models now.
 
@@ -29,7 +29,7 @@ The Neural Graphics Model Gym is an open-source toolkit for fine-tuning and expo
 Model Gym gives you:
 
 - A training and evaluation API built on PyTorch
-- Model export to .vgf for real-time use in game development
+- Model export to .vgf using ExecuTorch for real-time use in game development
 - Support for quantization-aware training (QAT) and post-training quantization (PTQ) using ExecuTorch
 - Optional Docker setup for reproducibility
 

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/3-model-training.md

@@ -6,7 +6,7 @@ weight: 4
 layout: learningpathall
 ---
 
-In this section, you'll get hands-on with how you can use the model gym to train your own models, or fine-tune the NSS use-case.
+In this section, you'll get hands-on with how you can use the model gym to fine-tune the NSS use-case.
 
 ## About NSS
 
@@ -54,7 +54,7 @@ neural-graphics-model-gym-examples/model_training_example.ipynb
 
 Step through the notebook for training.
 
-Once your model is trained, the next step is evaluation. You'll measure accuracy, compare checkpoints, and prepare the model for export. Open the evaluation notebook.
+Once your model is trained, the next step is evaluation. You'll measure accuracy, compare checkpoints, and prepare the model for export. Open the evaluation notebook located at the following location:
 
 ```output
 neural-graphics-model-gym-examples/model_evaluation_example.ipynb

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/4-model-explorer.md

@@ -56,4 +56,4 @@ Use the file browser to open the `.vgf` model exported earlier in your training
 
 Through this Learning Path, you’ve learned what neural graphics is and why it matters for game performance. You’ve stepped through the process of training and evaluating an NSS model using PyTorch and the Model Gym, and seen how to export that model into VGF (.vgf) for real-time deployment. You’ve also explored how to visualize and inspect the model’s structure using Model Explorer.
 
-As a next step, you can head over to the [Model Training Gym repository](https://github.com/arm/neural-graphics-model-gym/tree/main) and explore the documentation to explore integration into your own game development workflow. You’ll find resources on fine-tuning, deeper details about the training and export process, and everything you need to adapt to your own content and workflows.
+As a next step, you can head over to the [Model Training Gym repository](https://github.com/arm/neural-graphics-model-gym/tree/main) documentation to explore integration into your own game development workflow. You’ll find resources on fine-tuning, deeper details about the training and export process, and everything you need to adapt to your own content and workflows.

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/_index.md

@@ -9,7 +9,7 @@ learning_objectives:
     - Understand the principles of neural graphics and how it’s applied to game performance
     - Learn how to fine-tune and evaluate a neural network for Neural Super Sampling (NSS)
     - Use the Model Gym Python API and CLI to configure and train neural graphics models
-    - Visualize and inspect models using the Model Explorer tool
+    - Visualize and inspect `.vgf` models using the Model Explorer tool
 
 prerequisites:
     - Basic understanding of PyTorch and machine learning concepts