Reinforcement fine-tuning on Amazon Bedrock: best practices

This article provides comprehensive best practices for Reinforcement Fine-Tuning (RFT) on Amazon Bedrock, demonstrating how to customize foundation models using reward signals instead of labeled datasets.

• RFT achieves up to 66% accuracy gains over base models with reduced customization cost
• Most effective for tasks with verifiable correctness or subjective evaluation by AI judges
• Dataset size: 100-10,000 samples; start small (100-200) to validate reward signals
• Reward functions can be rule-based (RLVR) or model-based judges (RLAIF)
• Key dataset principles: diverse prompts, clear instructions, reliable reference answers, consistent rewards
• Optimal learning rate: 1e-4 for LoRA-based RFT across most use cases
• Batch size 128 works well; adjust based on loss stability and iteration speed
• Monitor training metrics: rewards should increase, entropy should remain stable, episode length patterns indicate learning efficiency
• Common pitfalls: reward hacking and reward variance; mitigate through rigorous normalization and comprehensive reward design
• Early stopping enabled by default; evaluation interval automatically calculated for efficiency

RFT enables significant model improvements across code generation, math reasoning, structured extraction, and content moderation when datasets are well-structured and reward functions capture desired quality.