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Overcoming reward signal challenges: Verifiable rewards-based reinforcement learning with GRPO on SageMaker AI
A practical guide to implementing Reinforcement Learning with Verifiable Rewards (RLVR) combined with Group Relative Policy Optimization (GRPO) on Amazon SageMaker AI. Using the GSM8K math dataset and a Qwen2.5-0.5B model, the approach employs a dual-reward system (format + correctness) and few-shot prompting to achieve a 3.7x accuracy improvement (11% → 41%) over the base model. The post covers dataset preparation, reward function design, QLoRA-based fine-tuning with DeepSpeed ZeRO-3, and how the technique generalizes to code generation and domain-specific text tasks.
Source: https://aws.amazon.com/blogs/machine-learning/overcoming-reward-signal-challenges-verifiable-rewards-based-reinforcement-learning-with-grpo-on-sagemaker-ai. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.
