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Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning

Paper · 2026 · Fengran Mo, Yifan Gao, Sha Li et al.

Summary

Multi-turn conversational search is usually handled by a fixed "rewrite, retrieve, generate" chain of separately trained components. Each component needs its own supervision, and the chain rarely learns when to ask for clarification or decline to answer. Deep search agents such as Search-R1 do combine reasoning with retrieval, but they assume self-contained, single-turn questions. The authors propose ConvAgent, a single agent that alternates reasoning and search across dialogue turns and is trained with reinforcement learning (GRPO).

Instead of relying only on a sparse final-answer signal, the reward is split into three parts:

  • outcome reward: answer quality;
  • search optimization (information gain) reward: how much the retrieved passages overlap with the gold answer;
  • mixed-initiative action reward: whether the agent correctly chooses to clarify, refuse, or answer.

On four common conversational benchmarks, including TopiOCQA, INSCIT and QReCC, ConvAgent beats strong baselines. It also needs far fewer reasoning tokens than a comparable agentic model. This matters because it shows that one end-to-end agent can handle context-dependent intent without gold rewritten queries as training signals.

Key points

  • The main backbone is Qwen-2.5-7b, with training via GRPO. Swapping GRPO for standard PPO consistently lowered results and made training less stable.
  • In ablations, the information gain reward helped more than the mixed-initiative reward. The latter brought little on QReCC, which has no clarification or refusal cases. Removing both rewards caused a steep drop on every dataset.
  • Emitting separate <clarify>, <noanswer> and <answer> tags worked better than packing all action content into one span. A backbone trained without the action reward almost never takes such actions.
  • In the per-turn analysis on TopiOCQA and INSCIT, ConvAgent outperforms Search-R1 and uses roughly a third of the reasoning tokens of the AgenticLM baseline.
  • Unlike the concurrent ChatR1 and ConvRecR1, the method needs no gold rewritten queries. The stated limitations are extra training and inference cost, a limited range of backbones, and fixed reward hyperparameters.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of Massachusetts AmherstAmherst Center · United States106 works · 66,692 citations🎓Université de MontréalMontreal · Canada68 works · 79,382 citations🏢AmazonSeattle · United States112 works · 40,565 citations🎓University of Notre DameNotre Dame · United States51 works · 36,717 citations🎓Northeastern UniversityBoston · United States57 works · 6,533 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1,426 worksDialogue systems · 624 works

Cite

Cite as Fengran Mo, Yifan Gao, Sha Li, Hansi Zeng, Xin Liu, Zhaoxuan Tan, Xian Li, Jianshu Chen, Dakuo Wang and Meng Jiang. 2026. Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.443

@inproceedings{mo2026agentic,
  title = {{Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning}},
  author = {Fengran Mo, Yifan Gao, Sha Li, Hansi Zeng, Xin Liu, Zhaoxuan Tan, Xian Li, Jianshu Chen, Dakuo Wang and Meng Jiang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.443},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.443}
}

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