Accelerate agentic tool calling with serverless model customization in Amazon SageMaker AI
Amazon AWS AI Blog / 4/7/2026
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Key Points
- The post explains a workflow for fine-tuning Qwen 2.5 7B Instruct specifically for tool calling in agentic systems using RLVR (reinforcement learning via reward signals).
- It details dataset preparation across three different agent behavior patterns, emphasizing coverage of varied tool-calling scenarios.
- It describes how reward functions are designed with tiered scoring to guide learning toward correct and higher-quality tool use.
- It covers the training configuration, how to interpret results, and evaluation on held-out data that includes unseen tools to test generalization.
- It concludes with deployment steps, connecting the customization and evaluation process to a serverless deployment approach via Amazon SageMaker AI.
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 design with tiered scoring, training configuration and results interpretation, evaluation on held-out data with unseen tools, and deployment.
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