O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916758737125376 |
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| author | Mei, Jianbiao Hu, Tao Fu, Daocheng Wen, Licheng Yang, Xuemeng Wu, Rong Cai, Pinlong Cai, Xinyu Gao, Xing Yang, Yu Xie, Chengjun Shi, Botian Liu, Yong Qiao, Yu |
| author_facet | Mei, Jianbiao Hu, Tao Fu, Daocheng Wen, Licheng Yang, Xuemeng Wu, Rong Cai, Pinlong Cai, Xinyu Gao, Xing Yang, Yu Xie, Chengjun Shi, Botian Liu, Yong Qiao, Yu |
| contents | Large Language Models (LLMs), despite their advancements, are fundamentally limited by their static parametric knowledge, hindering performance on tasks requiring open-domain up-to-date information. While enabling LLMs to interact with external knowledge environments is a promising solution, current efforts primarily address closed-end problems. Open-ended questions, which characterized by lacking a standard answer or providing non-unique and diverse answers, remain underexplored. To bridge this gap, we present O$^2$-Searcher, a novel search agent leveraging reinforcement learning to effectively tackle both open-ended and closed-ended questions in the open domain. O$^2$-Searcher leverages an efficient, locally simulated search environment for dynamic knowledge acquisition, effectively decoupling the external world knowledge from model's sophisticated reasoning processes. It employs a unified training mechanism with meticulously designed reward functions, enabling the agent to identify problem types and adapt different answer generation strategies. Furthermore, to evaluate performance on complex open-ended tasks, we construct O$^2$-QA, a high-quality benchmark featuring 300 manually curated, multi-domain open-ended questions with associated web page caches. Extensive experiments show that O$^2$-Searcher, using only a 3B model, significantly surpasses leading LLM agents on O$^2$-QA. It also achieves SOTA results on various closed-ended QA benchmarks against similarly-sized models, while performing on par with much larger ones. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_16582 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering Mei, Jianbiao Hu, Tao Fu, Daocheng Wen, Licheng Yang, Xuemeng Wu, Rong Cai, Pinlong Cai, Xinyu Gao, Xing Yang, Yu Xie, Chengjun Shi, Botian Liu, Yong Qiao, Yu Computation and Language Artificial Intelligence Large Language Models (LLMs), despite their advancements, are fundamentally limited by their static parametric knowledge, hindering performance on tasks requiring open-domain up-to-date information. While enabling LLMs to interact with external knowledge environments is a promising solution, current efforts primarily address closed-end problems. Open-ended questions, which characterized by lacking a standard answer or providing non-unique and diverse answers, remain underexplored. To bridge this gap, we present O$^2$-Searcher, a novel search agent leveraging reinforcement learning to effectively tackle both open-ended and closed-ended questions in the open domain. O$^2$-Searcher leverages an efficient, locally simulated search environment for dynamic knowledge acquisition, effectively decoupling the external world knowledge from model's sophisticated reasoning processes. It employs a unified training mechanism with meticulously designed reward functions, enabling the agent to identify problem types and adapt different answer generation strategies. Furthermore, to evaluate performance on complex open-ended tasks, we construct O$^2$-QA, a high-quality benchmark featuring 300 manually curated, multi-domain open-ended questions with associated web page caches. Extensive experiments show that O$^2$-Searcher, using only a 3B model, significantly surpasses leading LLM agents on O$^2$-QA. It also achieves SOTA results on various closed-ended QA benchmarks against similarly-sized models, while performing on par with much larger ones. |
| title | O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.16582 |