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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.24126 |
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| _version_ | 1866915582398431232 |
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| author | Kalyan, Vivek Andrews, Martin |
| author_facet | Kalyan, Vivek Andrews, Martin |
| contents | Large Language Model (LLM) agents can leverage multiple turns and tools to solve complex tasks, with prompt-based approaches achieving strong performance. This work demonstrates that Reinforcement Learning (RL) can push capabilities significantly further by learning from experience. Through experiments on a legal document search benchmark, we show that our RL-trained 14 Billion parameter model outperforms frontier class models (85% vs 78% accuracy). In addition, we explore turn-restricted regimes, during training and at test-time, that show these agents achieve better results if allowed to operate over longer multi-turn horizons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Long-Horizon Multi-Turn Search Agents Kalyan, Vivek Andrews, Martin Computation and Language Large Language Model (LLM) agents can leverage multiple turns and tools to solve complex tasks, with prompt-based approaches achieving strong performance. This work demonstrates that Reinforcement Learning (RL) can push capabilities significantly further by learning from experience. Through experiments on a legal document search benchmark, we show that our RL-trained 14 Billion parameter model outperforms frontier class models (85% vs 78% accuracy). In addition, we explore turn-restricted regimes, during training and at test-time, that show these agents achieve better results if allowed to operate over longer multi-turn horizons. |
| title | Reinforcement Learning for Long-Horizon Multi-Turn Search Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.24126 |