pith:EYMHDPD5
ToolRL: Reward is All Tool Learning Needs
A principled reward design for tool-use tasks lets reinforcement learning outperform supervised fine-tuning in training LLMs to use tools.
arxiv:2504.13958 v1 · 2025-04-16 · cs.LG · cs.AI · cs.CL
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Claims
Empirical evaluations across diverse benchmarks demonstrate that our approach yields robust, scalable, and stable training, achieving a 17% improvement over base models and a 15% gain over SFT models.
The explored reward strategies and the proposed principled design are assumed to transfer to tool-use scenarios outside the specific benchmarks and tool sets used in the experiments.
A principled reward design for tool selection and application in RL-trained LLMs delivers 17% gains over base models and 15% over SFT across benchmarks.
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| First computed | 2026-05-18T03:22:05.942883Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
261871bc7dc3367f5ed38716826c0f459c73573a005c87b75c51d4dcf1edc70c
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· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/EYMHDPD5YM3H6XWTQ4LIE3APIW \
| jq -c '.canonical_record' \
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Canonical record JSON
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