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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.17931 |
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| _version_ | 1866917427061719040 |
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| author | Li, Wanli Qu, Bince Pan, Bo Zhang, Jianyu Liu, Zheng Zhang, Pan Chen, Wei Zhang, Bo |
| author_facet | Li, Wanli Qu, Bince Pan, Bo Zhang, Jianyu Liu, Zheng Zhang, Pan Chen, Wei Zhang, Bo |
| contents | Reinforcement Learning (RL) has emerged as a powerful training paradigm for LLM-based agents. However, scaling agentic RL for deep research remains constrained by two coupled challenges: hand-crafted synthetic data fails to elicit genuine real-world search capabilities, and real-world search dependency during RL training introduces instability and prohibitive cost, which limits the scalability of Agentic RL. LiteResearcher is a training framework that makes Agentic RL scalable: by constructing a lite virtual world that mirrors real-world search dynamics, we enable a continuously improving training recipe that empowers a tiny search agent to outperform large-scale open-source and commercial models (e.g., Tongyi DeepResearch and Claude-4.5 Sonnet). Specifically, on common benchmarks such as GAIA and Xbench, our LiteResearcher-4B achieves open-source state-of-the-art results of 71.3% and 78.0% respectively, demonstrating that scalable RL training is a key enabler for Deep Research Agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17931 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent Li, Wanli Qu, Bince Pan, Bo Zhang, Jianyu Liu, Zheng Zhang, Pan Chen, Wei Zhang, Bo Artificial Intelligence Reinforcement Learning (RL) has emerged as a powerful training paradigm for LLM-based agents. However, scaling agentic RL for deep research remains constrained by two coupled challenges: hand-crafted synthetic data fails to elicit genuine real-world search capabilities, and real-world search dependency during RL training introduces instability and prohibitive cost, which limits the scalability of Agentic RL. LiteResearcher is a training framework that makes Agentic RL scalable: by constructing a lite virtual world that mirrors real-world search dynamics, we enable a continuously improving training recipe that empowers a tiny search agent to outperform large-scale open-source and commercial models (e.g., Tongyi DeepResearch and Claude-4.5 Sonnet). Specifically, on common benchmarks such as GAIA and Xbench, our LiteResearcher-4B achieves open-source state-of-the-art results of 71.3% and 78.0% respectively, demonstrating that scalable RL training is a key enabler for Deep Research Agents. |
| title | LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.17931 |