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About this paper
Yuanqing Yu, Zhefan Wang, Weizhi Ma, Shuai Wang, Chuhan Wu, Zhiqiang Guo, Min Zhang
StepTool introduces a step-grained reinforcement learning framework that models multi-step tool usage in large language models as a Markov Decision Process. This approach significantly enhances task completion rates, yielding up to a 19.16% improvement in success rate on benchmarks compared to prior methods, while also demonstrating model-agnostic effectiveness across various LLM backbones.
Key insights
- Reframing LLM tool learning as a sequential decision-making process, rather than a text generation task, is crucial for improving multi-step tool usage capabilities.
- Providing fine-grained, intermediate rewards for each tool invocation significantly enhances the learning signal, enabling LLMs to make more effective and relevant decisions throughout a multi-step task.
- Optimizing LLM policies with these step-grained rewards across entire trajectories fosters genuine knowledge discovery, allowing models to learn new and more robust tool-use strategies beyond just re-weighting existing ones.
Summary by alphaXiv.
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