Accelerate agentic tool calling with serverless model customization in Amazon SageMaker AI

This article explains how to fine-tune models for agentic tool calling using Reinforcement Learning with Verifiable Rewards (RLVR) in Amazon SageMaker AI.

• RLVR improves tool calling by generating multiple candidate responses and reinforcing correct ones
• Fine-tuned Qwen 2.5 7B achieved 57% improvement in tool call reward over base model
• Training dataset covered three behaviors: execute tool calls, ask for clarification, refuse harmful requests
• Reward function uses tiered scoring (1.0, 0.5, 0.0) to guide model learning
• Evaluation on unseen tools and scenarios showed strong generalization
• Deployment options include SageMaker endpoints or Amazon Bedrock
• Training completed in approximately 40 minutes with serverless infrastructure

SageMaker AI's serverless model customization eliminates infrastructure management overhead while enabling production-ready agentic AI systems through verifiable reward-based training.