The authors model tool selection as a multi-turn search problem rather than asking a language model to choose from a small, predefined list.
Reinforcement learning improves tool selection across large repositories
ToolSearcher trains language models to search iteratively, distinguish similar tools and allocate credit across the full selection process.
Chinese Tech
Zhejiang University · Ant Group
Research Digest··2 min read
Dai et al.
Why this paper
From Ant Group and Zhejiang University · Released code · Part of Skill Selection for Agents, now 15 papers
In one line
ToolSearcher uses reinforcement learning to optimize multi-turn tool selection at scale, outperforming existing methods.
What it released
Code
What we could check
- ✓Code link in the paper (github.com)
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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