In December 2025, we announced the availability of Reinforcement fine-tuning (RFT) on Amazon Bedrock starting with support for Nova models. This was followed by extended support for Open weight models such as OpenAI GPT OSS 20B and Qwen 3 32B in February 2026. RFT in Amazon Bedrock automates the end-to-end customization workflow. This allows the models to learn from feedback on multiple possible responses using a small set of prompts, rather than traditional large training datasets.
In this post, we walk through the end-to-end workflow of using RFT on Amazon Bedrock with OpenAI-compatible APIs: from setting up authentication, to deploying a Lambda-based reward function, to kicking off a training job and running on-demand inference on your fine-tuned model. Here, we use the GSM8K math dataset as our working example and target OpenAI’s gpt-oss-20B model hosted on Bedrock.
How reinforcement fine-tuning works
Reinforcement Fine-Tuning (RFT) represents a…
