Setting up reward functions - Amazon Bedrock
Reinforcement Learning via Verifiable Rewards (RLVR)Reinforcement Learning via AI Feedback (RLAIF)

Setting up reward functions

Reward functions evaluate response quality and provide feedback signals for model training. Choose the approach that matches your task requirements.

Reinforcement Learning via Verifiable Rewards (RLVR)

RLVR enables you to optimize models for objective tasks such as code generation or math reasoning. You can define reward functions using verifiable rule-based graders or use ready-to-use templates for common use cases such as format checks, summarization, and text similarity.

You have two options for RLVR (Custom Code):

  • Use console-provided templates - The Amazon Bedrock console provides sample templates for grader Lambda functions:

    • Mathematical reasoning with ground truth verification

    • Format validation and constraint checking

    • Generic grader Lambda template with boilerplate code for your grader Lambda function

    Before setting up your Lambda function, follow the instructions in the provided template on the Create RFT job page in the Amazon Bedrock console.

  • Bring your own Lambda function — Create custom reward functions executed through Lambda functions using your own Lambda ARN. You can combine multiple graders to produce a single score.

Reinforcement Learning via AI Feedback (RLAIF)

RLAIF enables optimization for subjective tasks such as instruction following or chatbot interactions. You can use AI-based judges with ready-to-use templates for common use cases to evaluate response quality based on criteria you define.

For RLAIF (Model as Judge):

  • Select an Amazon Bedrock hosted base Model as Judge

  • Configure instructions for evaluation

  • Define evaluation criteria and scoring guidelines

You can use the LLM-as-Judge prompt templates provided in the Amazon Bedrock console:

  • Instruction following (Judge model training)

  • Summarization (Multi-turn dialogs)

  • Reasoning evaluation (CoT for specialized domains)

  • RAG faithfulness (Context-grounded Q&A)

Note

  • When you use the console's Model as Judge option, Amazon Bedrock automatically converts your configuration into a Lambda function that executes during training.

  • If you bring your own Lambda function, the Lambda execution role needs necessary permissions to invoke models with model ID or inference profile as described in Grader Lambda function permissions for RLAIF.