Training Gemma-3 for Structured Mathematical Reasoning with Tunix GRPO, LoRA Adapters, and GSM8K Rewards

In this tutorial, we build an end-to-end GRPO training workflow that teaches Gemma-3 to reason through GSM8K math problems using Tunix , JAX, LoRA, and custom reward functions. We start by preparing the environment, authenticating with Hugging Face, loading the Gemma-3 model, and wrapping GSM8K examples into a prompt format that requires both structured reasoning and a final numeric answer. We then define reward functions that assess format adherence and mathematical correctness, attach LoRA adapters to keep training lightweight, evaluate the baseline model, and run GRPO to improve the policy via group-sampled generations. It provides a reinforcement learning tutorial in which we train only the adapter weights while keeping the workflow compact enough for a single-accelerator setup.

We set up the complete Colab environment by installing Tunix, JAX, Flax, Qwix, TensorFlow, datasets, and the supporting notebook utilities needed for GRPO training. We configure authentication, disable unnecessary logging paths, keep TensorFlow away from the accelerator, and verify that JAX can see the available TPU or GPU devices. We also define the core training hyperparameters, LoRA settings, generation limits, checkpoint paths, and device mesh that control how the model trains across the available hardware.

We define the structured reasoning format that asks the model to place its reasoning inside reasoning tags and its final numeric answer inside answer tags. We load GSM8K from Hugging Face, extract the ground-truth answers, and convert each math problem into the prompt format expected by the GRPO rollout pipeline. We then create reward functions that score exact format matching, approximate tag usage, answer correctness, and fallback numeric extraction so the model receives useful feedback from multiple signals.

We download the selected Gemma-3 checkpoint, create the base model from safetensors, and prepare the tokenizer and EOS token list for generation. We attach LoRA adapters to the attention and MLP projection modules, enabling us to train a lightweight policy without updating the full model weights. We also build a sampler-based evaluation function and run a baseline test before GRPO training to measure the model’s initial accuracy and adherence to the format.

We create the learning-rate schedule, optimizer, gradient clipping, and AdamW configuration that guide the LoRA policy updates during training. We define the Tunix RL cluster with actor, reference, and rollout roles, then connect the rollout configuration to the tokenizer, generation limits, sampling temperature, and EOS tokens. We initialize the GRPO learner, launch TensorBoard for live metrics, and start the GRPO training loop on the prepared GSM8K batches.