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home / papers / 2026 / One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement

One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement

Paper · 2026 · Yixiao Zhou, Dongzhou Cheng, Zhiliang wu et al.

Summary

LLMs often fail to use reasoning skills they already have, because people phrase questions loosely while models perform best on explicit, well-structured problem statements. Fine-tuning each model separately scales poorly and is impossible for closed models. Fixed prompt templates cannot restructure individual queries. The authors propose ReQueR, a separate Refiner LLM that rewrites each question into a clearer logical form and then passes it to an unchanged Solver model. The Refiner is trained first with SFT and then with reinforcement learning. Its reward depends on whether frozen Solvers answer the rewritten query correctly. Two mechanisms stabilize training. The Adaptive Solver Hierarchy (ASH) is a curriculum based on Vygotsky's Zone of Proximal Development that gives each sample a stronger or weaker Solver depending on recent success. A perplexity-based penalty flags rewrites that leak the answer. Across architectures and benchmarks, ReQueR adds 1.7–7.2 points absolute and beats strong baselines by 2.1 points on average. Its main value is reuse: one Refiner trained on a few small open models also helps unseen models without changing their weights.

Key points

  • Alignment cost moves from O(N) per-model tuning to an O(1) inference-time frontend. The Refiner (a 4B model) rewrites queries per sample using strategies such as variable mapping, removal of irrelevant narrative, disambiguation, and reduction of concepts to primitive operations.
  • ASH ranks a pool of frozen Solvers by capability and raises or lowers each sample's Solver difficulty after repeated failure or easy success. This keeps rewards dense and limits overfitting to one model.
  • In the ablation on GSM-Plus, Qwen3-0.6B rises from 45.28 to 61.57 with the full method. SFT contributes +5.14 and RL a further +11.15.
  • Other ablation results:
    • Removing ASH costs 5.85 points for Llama-3.1-8B-Instruct on MATH-500.
    • Removing the leak penalty costs 2.15 points for Qwen3-0.6B on GSM-Plus.
    • Qwen2.5-72B-Instruct reaches 83.00 on MATH-500.
  • Refined queries shorten Solver outputs by 4.8–17.3%, which partly offsets the added refinement step. Limitations include possible cascading errors and the need for tasks with verifiable answers. Code is available at github.com/newera-xiao/ReQueR.

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

🎓Zhejiang UniversityHangzhou · China172 works · 12,988 citations🎓Chinese University of Hong KongHong Kong · China126 works · 14,073 citations🎓Shanghai Innovation InstituteShanghai · China24 works · 262 citations🎓Southeast UniversityNanjing · China28 works · 354 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 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Yixiao Zhou, Dongzhou Cheng, Zhiliang wu, Yi Yang, Yu Cheng and Hehe Fan. 2026. One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1807

@inproceedings{zhou2026one,
  title = {{One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement}},
  author = {Yixiao Zhou, Dongzhou Cheng, Zhiliang wu, Yi Yang, Yu Cheng and Hehe Fan},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1807},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1807}
}

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