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Gemma-3 enhanced for math reasoning with GRPO and LoRA

By PulseAugur Editorial · [1 sources] ·

This tutorial details how to train the Gemma-3 model to improve its structured mathematical reasoning capabilities using the GSM8K dataset. The process involves setting up the environment with tools like Tunix, JAX, and LoRA adapters, and then applying Grouped-Sampled Policy Optimization (GRPO) with custom reward functions. The training focuses on optimizing only the adapter weights, making the workflow efficient enough for a single-accelerator setup. AI

IMPACT Enhances Gemma-3's mathematical reasoning, potentially improving its utility in educational and scientific applications.

RANK_REASON Tutorial on training a specific model (Gemma-3) for a specific task (mathematical reasoning) using particular techniques (GRPO, LoRA, GSM8K rewards). [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Gemma-3 enhanced for math reasoning with GRPO and LoRA

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Tutorial on training a specific model (Gemma-3) for a specific task (mathematical reasoning) using particular techniques (GRPO, LoRA, GSM8K rewards). [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. MarkTechPost TIER_1 English(EN) · Sana Hassan ·

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

    <p>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 form…

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