Amazon Bedrock now offers reinforcement fine-tuning as a way to customize AI models without needing labeled input-output pairs, instead using reward functions to guide model learning and achieving accuracy improvements of up to 66 percent on average compared to base models. But getting those results requires understanding what data you actually have, how to structure it, and which models support the technique.

What Is Reinforcement Fine-Tuning and Why Does It Matter?

Reinforcement fine-tuning represents a different approach to customizing large language models, or LLMs, compared to traditional supervised learning. Instead of providing labeled examples that show the model "input A should produce output B," you define reward functions that evaluate how good a response is. The model then learns iteratively by receiving feedback scores from those reward functions, gradually improving its performance on your specific task.

This matters because many organizations don't have neatly labeled datasets ready to go. They may have domain expertise, clear quality criteria, or the ability to evaluate outputs programmatically, but not a pre-built training set. Reinforcement fine-tuning unlocks customization for those scenarios.

What Data and Setup Do You Actually Need?

Before diving into reinforcement fine-tuning, you need to honestly assess what you have on hand. The source material breaks this down into three scenarios:

• Labeled Pairs: You have input-output examples that define correct answers for your task. In this case, supervised fine-tuning is the right choice, not reinforcement fine-tuning.
• Objective Quality Metrics: You can evaluate output quality using code or automated checks, such as format validation or test case passing. This is where reinforcement fine-tuning shines, because you can encode those checks into reward functions.
• No Training Data: You have access to a large model that already handles your task well. You can use that model as a teacher to generate training data for a smaller student model through distillation.

The critical insight is that having data is not the same as having it in the right shape. Amazon Bedrock accepts a maximum of 20,000 prompts for reinforcement fine-tuning, and the system prompt used during training must match the system prompt used during inference, because the model learns the prompt as context that triggers its fine-tuned behavior.

How Should You Structure Your Reward Functions?

The reward function is the engine of reinforcement fine-tuning. Instead of labeled examples, you provide a scoring mechanism that tells the model whether its output is good or bad. Amazon Bedrock offers two approaches: you can write custom Lambda functions, or use the "Model as Judge" option, which automatically converts your configuration into a Lambda function during training.

For subjective tasks like instruction following or chatbot interactions, you can call Amazon Bedrock foundation models as judges within your Lambda function to evaluate response quality based on your criteria. This is particularly useful when there is no single correct answer, but you can define what "good" looks like.

However, reward signals matter enormously. If rewards are consistently zero percent, it means your model is not learning anything useful. In that case, supervised fine-tuning should come first to establish basic capabilities. Conversely, if rewards are greater than 95 percent, reinforcement fine-tuning might be unnecessary, because the model is already performing at ceiling.

Which Models Support Reinforcement Fine-Tuning?

Not all models on Amazon Bedrock support reinforcement fine-tuning. The technique has the narrowest surface area of all customization methods. Currently, reinforcement fine-tuning is available for Amazon's Nova 2 Lite model in the US East (N. Virginia) region, and for OpenAI's gpt-oss-20B and Qwen's Qwen3 32B models in the US West (Oregon) region.

This is a significant limitation. If you want to use reinforcement fine-tuning with Anthropic's Claude models, you cannot, because distillation is not currently available for Anthropic models on Amazon Bedrock, and there is no confirmed timeline for when that support will be restored.

What Happens to Your Training Data After Fine-Tuning?

A common concern with fine-tuning is data privacy. Amazon Bedrock does not store your training or validation data after the fine-tuning job completes. However, there is an important caveat: fine-tuned models can replay some of the fine-tuning data while generating completions. If your application should not expose fine-tuning data in any form, you should filter out confidential data from your training data before submitting it.

Once fine-tuning is complete, you can use the resulting fine-tuned model for on-demand inference through Amazon Bedrock's OpenAI-compatible APIs without additional deployment steps. The model is available only in the region and account in which it was created, and the region is fixed at training time, not at serving time.

How to Prepare for Reinforcement Fine-Tuning on Amazon Bedrock

• Assess Your Data Type: Determine whether you have labeled pairs, objective quality metrics, or neither. Only proceed with reinforcement fine-tuning if you can define reward functions that evaluate output quality programmatically or through a model-based judge.
• Design Your Reward Function: Write or configure a Lambda function that scores model outputs on your criteria. For subjective tasks, use Amazon Bedrock foundation models as judges to evaluate responses based on your specific requirements.
• Check Model and Region Availability: Verify that your desired model supports reinforcement fine-tuning and is available in a supported region. Currently, options are limited to Amazon Nova 2 Lite in US East, or OpenAI and Qwen models in US West.
• Prepare Training Prompts: Compile up to 20,000 prompts that represent the tasks you want the model to improve on. Ensure the system prompt matches what you will use during inference.
• Filter Sensitive Data: Remove any confidential or sensitive information from your training data, since fine-tuned models may replay portions of it during generation.

The 66 percent accuracy improvement that Amazon Bedrock reports is compelling, but it is not automatic. It depends on having well-designed reward functions, sufficient training prompts, and realistic expectations about what the model can learn. Organizations considering reinforcement fine-tuning should start by honestly assessing whether their use case fits the requirements, and whether their data is truly ready.