Training Gemma-3 for Structured Mathematical Reasoning with Tunix GRPO, LoRA Adapters, and GSM8K Rewards
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We build an end-to-end GRPO training workflow that teaches Gemma-3 to reason through GSM8K math problems. We prepare the environment, authenticate with Hugging Face, load Gemma-3, and wrap examples into a reasoning-plus-answer prompt format. We define reward functions for format adherence and numeric correctness, then attach LoRA adapters to keep training lightweight. We evaluate a baseline, run GRPO to improve the policy through group-sampled generations, and optionally export the merged model.
1Key Takeaways
- We build an end-to-end GRPO training workflow that teaches Gemma-3 to reason through GSM8K math problems.
- We prepare the environment, authenticate with Hugging Face, load Gemma-3, and wrap examples into a reasoning-plus-answer prompt format.
- We define reward functions for format adherence and numeric correctness, then attach LoRA adapters to keep training lightweight.
- We evaluate a baseline, run GRPO to improve the policy through group-sampled generations, and optionally export the merged model.
2AIWedia Score
9.2/10
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3Why it matters
New model releases change what is possible for builders, researchers, and everyday AI users. MarkTechPost reports that we build an end-to-end GRPO training workflow that teaches Gemma-3 to reason through GSM8K math problems.
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