Update learning path for Model Training Gym

Author: madeline-underwood

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

@@ -8,29 +8,37 @@ layout: learningpathall
 
 ## What is Neural Graphics?
 
-Neural graphics is an intersection of graphics and machine learning. Rather than relying purely on traditional GPU pipelines, neural graphics integrates learned models directly into the rendering stack. The techniques are particularly powerful on mobile devices, where battery life and performance constraints limit traditional compute-heavy rendering approaches. The goal is to deliver high visual fidelity without increasing GPU cost. This is achieved by training and deploying compact neural networks optimized for the device's hardware.
+Neural graphics is an intersection of graphics and machine learning. Rather than relying purely on traditional GPU pipelines, neural graphics integrates learned models directly into the rendering stack. These techniques are particularly powerful on mobile devices, where battery life and performance constraints limit traditional compute-heavy rendering approaches. Your goal is to deliver high visual fidelity without increasing GPU cost. You achieve this by training and deploying compact neural networks optimized for your device's hardware.
 
 ## How does Arm support neural graphics?
 
-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.
+
+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 you 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.
 
 
-You can develop neural graphics models using well-known ML frameworks like PyTorch, then export them for deployment with Arm's hardware-aware pipeline. The workflow converts your model to `.vgf` via the TOSA intermediate representation, making it possible to tailor model development for your game use case. In this Learning Path, you will focus on **Neural Super Sampling (NSS)** as the primary example for training, evaluating, and deploying neural models using the [**Neural Graphics Model Gym**](https://github.com/arm/neural-graphics-model-gym). To learn more about NSS, see the [resources on Hugging Face](https://huggingface.co/Arm/neural-super-sampling). Arm has also developed a set of Vulkan Samples to help you get started. The `.vgf` format is introduced in the `postprocessing_with_vgf` sample. For a broader overview of neural graphics developer resources including the Vulkan Samples, visit the [introductory Learning Path](/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/).
+
+You can develop neural graphics models using well-known ML frameworks like PyTorch, then export them for deployment with Arm's hardware-aware pipeline. The workflow converts your model to `.vgf` via the TOSA intermediate representation, making it possible to tailor model development for your game use case. In this Learning Path, you will focus on **Neural Super Sampling (NSS)** as the primary example for training, evaluating, and deploying neural models using the [**Neural Graphics Model Gym**](https://github.com/arm/neural-graphics-model-gym). To learn more about NSS, see the [resources on Hugging Face](https://huggingface.co/Arm/neural-super-sampling). Arm has also developed a set of Vulkan Samples to help you get started. The `.vgf` format is introduced in the `postprocessing_with_vgf` sample. For a broader overview of neural graphics developer resources, including the Vulkan Samples, visit the [introductory Learning Path](/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/).
+
 
 
 Starting in 2026, Arm GPUs will feature dedicated neural accelerators, optimized for low-latency inference in graphics workloads. To help you get started early, Arm provides the ML Emulation Layers for Vulkan that simulate future hardware behavior, so you can build and test models now.
 
 ## What is the Neural Graphics Model Gym?
 
+
 The Neural Graphics Model Gym is an open-source toolkit for fine-tuning and exporting neural graphics models. It is designed to streamline the entire model lifecycle for graphics-focused use cases, like NSS.
 
-Model Gym gives you:
+With Model Gym, you can:
+
+- Train and evaluate models using a PyTorch-based API
+- Export models to `.vgf` using ExecuTorch for real-time use in game development
+- Take advantage of quantization-aware training (QAT) and post-training quantization (PTQ) with ExecuTorch
+- Use an optional Docker setup for reproducibility
+
+You can choose to work with Python notebooks for rapid experimentation or use the command-line interface for automation. This Learning Path will walk you through the demonstrative notebooks and prepare you to start using the CLI for your own model development.
 
-- A training and evaluation API built on PyTorch
-- 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
 
-The toolkit supports workflows via both Python notebooks (for rapid experimentation) and command-line interface. This Learning Path will walk you through the demonstrative notebooks, and prepare you to start using the CLI for your own model development.
+You have completed the introduction! You're ready to set up your environment and start working with neural graphics models. Keep going!

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/2-devenv.md

@@ -55,4 +55,5 @@ print("Torch version:", torch.__version__)
 print("Model Gym version:", ng_model_gym.__version__)
 ```
 
-You’re now ready to start walking through the training and evaluation steps.
+You’ve completed your environment setup - great work! You’re now ready to start walking through the training and evaluation steps.
+

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

@@ -62,6 +62,8 @@ neural-graphics-model-gym-examples/tutorials/nss/model_evaluation_example.ipynb
 
 At the end you should see a visual comparison of the NSS upscaling and the ground truth image.
 
-Proceed to the final section to view the model structure and explore further resources.
+
+You’ve completed the training and evaluation steps. Proceed to the final section to view the model structure and explore further resources. 
+
 
 

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

@@ -46,4 +46,4 @@ 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) 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.
+You’ve completed the Learning Path. You now know how to train, export, and visualize neural graphics models on Arm. Explore the Model Training Gym repository for deeper integration and to keep building your skills.

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

@@ -50,6 +50,10 @@ further_reading:
         title: NSS on HuggingFace
         link: https://huggingface.co/Arm/neural-super-sampling
         type: website
+    - resource:
+        title: Vulkan ML Sample Learning Path
+        link: /learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/
+        type: learningpath
 
 
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