Overcoming reward signal challenges: Verifiable rewards-based reinforcement learning with GRPO on SageMaker AI

AI Impact Summary

This post details a method for improving reinforcement learning model training through verifiable rewards, leveraging Group Relative Policy Optimization (GRPO) and few-shot learning techniques. The core innovation is using programmatic, rule-based reward functions to eliminate reward hacking and provide transparent feedback, particularly effective for tasks like mathematical reasoning and code generation. By combining GRPO's group-aware optimization with few-shot examples and verifiable rewards, the approach aims to accelerate learning and improve model robustness, demonstrated through fine-tuning the Qwen2.5-0.5B model on the GSM8K dataset.