Reinforcement fine-tuning on Amazon Bedrock with OpenAI-Compatible APIs: a technical walkthrough

This article provides a technical walkthrough of Reinforcement Fine-Tuning (RFT) on Amazon Bedrock using OpenAI-compatible APIs, demonstrating how to customize foundation models through iterative feedback loops rather than traditional supervised learning.

• RFT enables models to learn from generated responses and reward feedback instead of static training datasets
• Key components: actor model, input states, output actions, and Lambda-based reward functions
• Amazon Bedrock handles GRPO optimization, batching, parallelization, and convergence detection automatically
• Six-step workflow: configure OpenAI client, upload training data via Files API, deploy Lambda reward function, create fine-tuning job, monitor training metrics, run on-demand inference
• Supports OpenAI GPT-OSS 20B, Qwen 3 32B, and other models with no endpoint provisioning required
• Training metrics include critic_rewards_mean, actor_entropy, actor_grad_norm, and response_length_mean
• GSM8K math dataset example shows reward improvement from 0.56 to 0.85-0.97 during training

Amazon Bedrock RFT simplifies enterprise-scale model customization by combining OpenAI SDK compatibility, Lambda-based grading, and serverless inference into a unified workflow.