reasoning-gym/examples/unsloth

Chain Sum LORA Training with unsloth

This example demonstrates how to fine-tune an LLM with RL on a reasoning gym environment using the unsloth framework. Unsloth is a efficient open-source library for fine-tuning & RL. Unsloths default training path uses quantised low rank adaption (QLORA) which results in a signficantly lower memory footprint (\approx 3x) and means you can significantly increase batch sizes and context length without risking OOM errors.

Requirements:

python >= 3.10

Installation

1. Install reasoning-gym:

   pip install reasoning-gym
   

2. Install unsloth dependencies:

   pip install -r requirements.txt
   

3. Run training script To start training with unsloth with RG environments using default arguments run the following:

   python train_grpo_lora.py
   

   To customise/override any default arguments you can simply:

   python train_grpo_lora.py  --dataset-name chain_sum --max-seq-length 512 --model-id Qwen/Qwen2.5-7B-Instruct
   
   

Note the free open-source version of unsloth is currently built to train models in single GPU environments only.