Agentic tool calling is what makes AI agents useful in production. It’s how they query databases, trigger workflows, retrieve real-time data, and act on a user’s behalf. But base models frequently hallucinate tools, pass bad parameters, and attempt actions when they should ask for clarification. These failures erode trust and block production deployment.
You can use Serverless model customization in Amazon SageMaker AI to fix these problems without managing infrastructure. With Reinforcement Learning with Verifiable Rewards (RLVR), the model generates its own candidate responses, receives a reward signal indicating quality, and updates its behavior to favor what works. You select a model, configure a technique, point to your data and reward function, and SageMaker AI handles the rest. In this post, we walk through how we fine-tuned Qwen 2.5 7B Instruct for tool calling using RLVR. We cover dataset preparation across three distinct agent behaviors, reward function…
