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

This article demonstrates how to implement Reinforcement Learning with Verifiable Rewards (RLVR) combined with Group Relative Policy Optimization (GRPO) on Amazon SageMaker AI to improve large language model training for mathematical reasoning tasks.

• RLVR uses programmatic reward functions to automatically score outputs against objective criteria, eliminating reward hacking
• GRPO optimizes performance within groups rather than globally, reducing variance and accelerating convergence
• Dual-reward system: format rewards (0.5 points) and correctness rewards (1.0 points) guide model learning
• Few-shot examples (8-shot) provide templates and narrow exploration space for faster learning
• Qwen2.5-0.5B model achieved 41% accuracy on GSM8K, a 3.7x improvement over 11% baseline
• Non-linear scaling: 8-shot prompting significantly outperformed 0-shot and 2-shot configurations
• Approach generalizes to code generation and domain-specific text with verifiable outputs
• Uses QLoRA for efficient training on SageMaker with distributed multi-GPU support

RLVR with GRPO provides an effective alternative to preference-based training for tasks with objectively verifiable outcomes, enabling rapid adaptation and robust performance across mathematical reasoning and similar domains.