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How to train math reasoning with GRPO with TRL

Use GRPOTrainer with the built-in accuracy_reward on a math dataset, the method behind DeepSeek R1.

Short answer: Install TRL with math support using pip install trl[math_verify]; then load trl-lib/DeepMath-103K and create a GRPOTrainer with model Qwen/Qwen2.5-0.5B-Instruct and reward_funcs=accuracy_reward; call trainer.train(). All steps.

cost. RL runs are long; budget GPU hours before starting.

Who it is forA researcher who wants to try reinforcement learning on a small instruct model.

The problemRL training setups are complex and memory hungry.

The outcomeA GRPO-trained checkpoint and a feel for reward-driven training.

What you need first

  • Python with pip
  • A GPU is strongly recommended; the README covers NVIDIA, AMD and AWS Trainium setups
  • Access to Hugging Face Hub models and datasets

Steps

  1. Install TRL with math support using pip install trl[math_verify]
  2. Load trl-lib/DeepMath-103K and create a GRPOTrainer with model Qwen/Qwen2.5-0.5B-Instruct and reward_funcs=accuracy_reward
  3. Call trainer.train()
  4. For reasoning models, switch to the reasoning_accuracy_reward function as the README notes

Prompt for your agent

Using huggingface/trl, install `pip install trl[math_verify]`, write a GRPOTrainer script on trl-lib/DeepMath-103K with accuracy_reward and Qwen/Qwen2.5-0.5B-Instruct, and add logging so I can watch the reward climb.

Caveat. GRPO is more memory efficient than PPO per the README, but still needs a capable GPU.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for huggingface/trl and "Train math reasoning with GRPO"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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