Implementing GRPO from Scratch with Distributed Reinforcement Learning on Qwen2.5-1.5B-Instruct

GRPO (Group Relative Policy Optimization) is a recent RL algorithm that discards the critic model and computes policy gradients via relative comparisons within a sample group, improving stability and learning efficiency.

The article introduces a step‑by‑step tutorial by AI engineer Andriy Burkov that implements GRPO from scratch for the Qwen2.5‑1.5B‑Instruct model, using a distributed training setup.

Key dependencies include PyTorch for tensor operations and multi‑GPU training, Hugging Face Transformers for model and tokenizer loading, FlashAttention2 for memory‑efficient attention, and Weights & Biases for experiment tracking.

The tutorial is organized into several parts: basic environment setup, data formatting and answer extraction, dataset preparation with GSM8K, evaluation functions that compare model outputs to ground‑truth answers, reward functions (correctness and format rewards), a full DataParallel implementation of GRPO, and the final training loop.

Training hyper‑parameters such as num_iterations, num_steps, batch_size, num_generations, max_completion_length, beta, learning_rate, mu and epsilon are detailed, and the process shows a jump from 23.33% to 90% accuracy after one GRPO iteration.

Additional notes discuss scaling to larger models with DeepSpeed or FSDP, and the limitation that the fine‑tuned model does not yet generate an EOS token after the tag.