Gemma-3 Trained for Math Reasoning Using GRPO and LoRA

A new tutorial demonstrates an end-to-end workflow for training Google's Gemma-3 language model to perform structured mathematical reasoning on GSM8K math problems. The process utilizes the Tunix framework, JAX, LoRA adapters for efficient training, and custom reward functions to guide the model's learning. The tutorial begins with setting up the environment, including authenticating with Hugging Face and loading the Gemma-3 model. It then details how to format GSM8K examples into prompts that require both step-by-step reasoning and a final numerical answer.

Custom reward functions are defined to evaluate the model's adherence to the required format and its mathematical accuracy. The use of LoRA adapters is highlighted as a method to keep the training process lightweight, allowing it to be performed on a single accelerator. Before initiating the reinforcement learning phase, the baseline performance of the Gemma-3 model is evaluated. The tutorial then proceeds with Grouped-Sampled Reinforcement Learning from Human Feedback (GRPO) to improve the model's policy by generating and evaluating multiple responses.

The GRPO training focuses specifically on updating only the adapter weights, a technique that significantly reduces computational requirements and memory usage. This approach ensures the workflow remains compact and accessible, even for users with limited hardware resources. The tutorial provides installation instructions for Tunix and the necessary JAX ecosystem components, including `ipywidgets`, `tensorboardX`, `transformers`, `grain`, `nest_asyncio`, `datasets`, `huggingface_hub`, and `tensorflow`. It also specifies the uninstallation and reinstallation of `flax` and `wandb` to ensure compatibility with the training setup. The process involves setting environment variables to disable `wandb` and optimize tokenization parallelism and TensorFlow logging.