reasoning-gym/examples/trl

TRL Examples

This directory contains examples using the TRL (Transformer Reinforcement Learning) library to fine-tune language models with reinforcement learning techniques.

GRPO Example

The main example demonstrates using GRPO (Group Relative Policy Optimization) to fine-tune a language model on reasoning tasks from reasoning-gym. It includes:

• Custom reward functions for answer accuracy and format compliance
• Integration with reasoning-gym datasets
• Configurable training parameters via YAML config
• Wandb logging and model checkpointing
• Evaluation on held-out test sets

Setup

1. Install the required dependencies:

pip install -r requirements.txt

Usage

1. Configure the training parameters in config/grpo.yaml
2. Run the training script:

python main_grpo_reward.py

The model will be trained using GRPO with the specified reasoning-gym dataset and evaluation metrics will be logged to Weights & Biases.