AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915520253526016 |
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| author | Xu, Ran Zhuang, Yuchen Dong, Zihan Wang, Jonathan Yu, Yue Ho, Joyce C. Zhang, Linjun Wang, Haoyu Shi, Wenqi Yang, Carl |
| author_facet | Xu, Ran Zhuang, Yuchen Dong, Zihan Wang, Jonathan Yu, Yue Ho, Joyce C. Zhang, Linjun Wang, Haoyu Shi, Wenqi Yang, Carl |
| contents | Search-augmented LLMs often struggle with complex reasoning tasks due to ineffective multi-hop retrieval and limited reasoning ability. We propose AceSearcher, a cooperative self-play framework that trains a single large language model (LLM) to alternate between two roles: a decomposer that breaks down complex queries and a solver that integrates retrieved contexts for answer generation. AceSearcher couples supervised fine-tuning on a diverse mixture of search, reasoning, and decomposition tasks with reinforcement fine-tuning optimized for final answer accuracy, eliminating the need for intermediate annotations. Extensive experiments on three reasoning-intensive tasks across 10 datasets show that AceSearcher outperforms state-of-the-art baselines, achieving an average exact match improvement of 7.6%. Remarkably, on document-level finance reasoning tasks, AceSearcher-32B matches the performance of the DeepSeek-V3 model using less than 5% of its parameters. Even at smaller scales (1.5B and 8B), AceSearcher often surpasses existing search-augmented LLMs with up to 9x more parameters, highlighting its exceptional efficiency and effectiveness in tackling complex reasoning tasks. Our code will be published at https://github.com/ritaranx/AceSearcher and https://huggingface.co/AceSearcher. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play Xu, Ran Zhuang, Yuchen Dong, Zihan Wang, Jonathan Yu, Yue Ho, Joyce C. Zhang, Linjun Wang, Haoyu Shi, Wenqi Yang, Carl Computation and Language Artificial Intelligence Information Retrieval Machine Learning Search-augmented LLMs often struggle with complex reasoning tasks due to ineffective multi-hop retrieval and limited reasoning ability. We propose AceSearcher, a cooperative self-play framework that trains a single large language model (LLM) to alternate between two roles: a decomposer that breaks down complex queries and a solver that integrates retrieved contexts for answer generation. AceSearcher couples supervised fine-tuning on a diverse mixture of search, reasoning, and decomposition tasks with reinforcement fine-tuning optimized for final answer accuracy, eliminating the need for intermediate annotations. Extensive experiments on three reasoning-intensive tasks across 10 datasets show that AceSearcher outperforms state-of-the-art baselines, achieving an average exact match improvement of 7.6%. Remarkably, on document-level finance reasoning tasks, AceSearcher-32B matches the performance of the DeepSeek-V3 model using less than 5% of its parameters. Even at smaller scales (1.5B and 8B), AceSearcher often surpasses existing search-augmented LLMs with up to 9x more parameters, highlighting its exceptional efficiency and effectiveness in tackling complex reasoning tasks. Our code will be published at https://github.com/ritaranx/AceSearcher and https://huggingface.co/AceSearcher. |
| title | AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play |
| topic | Computation and Language Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2509.24193 |