The Engineering Handbook for GRPO + LoRA with Verl: Training Qwen2.5 on Multi-GPU

The article details the process of setting up a high-performance Multi-GPU pipeline using GRPO and LoRA for training the Qwen2.5–3B-Instruct model, highlighting the engineering challenges and optimizations required to achieve efficient reinforcement learning with the Verl framework. It explores the shift from traditional PPO to GRPO, which reduces memory usage by eliminating the Critic model, and outlines the deployment of this setup on NVIDIA A100 GPUs, emphasizing the importance of managing VRAM utilization and communication overhead. Despite achieving significant training time reductions and stable system performance, the project reveals that the binary reward function drove the model towards efficiency rather than deep reasoning, and warns of the potential pitfalls of overfitting to specific prompt formats. The article underscores the importance of reward engineering and data diversity in future iterations to enhance the model's reasoning capabilities and adaptability to varied prompts.

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