@inproceedings{zhou-etal-2026-one,
    title = "One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement",
    author = "Zhou, Yixiao  and
      Cheng, Dongzhou  and
      wu, Zhiliang  and
      Yang, Yi  and
      Cheng, Yu  and
      Fan, Hehe",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1807/",
    pages = "38957--38978",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) often fail to utilize their latent reasoning capabilities due to a distributional mismatch between ambiguous human inquiries and the structured logic required for machine activation. Existing alignment methods either incur prohibitive $O(N)$ costs by fine-tuning each model individually or rely on static prompts that fail to resolve query-level structural complexity. In this paper, we propose **ReQueR** (**Re**inforcement **Que**ry **R**efinement), a modular framework that treats reasoning elicitation as an inference-time alignment task. We train a specialized Refiner policy via Reinforcement Learning to rewrite raw queries into explicit logical decompositions, treating frozen LLMs as the environment. Rooted in the classical Zone of Proximal Development from educational psychology, we introduce the Adaptive Solver Hierarchy, a curriculum mechanism that stabilizes training by dynamically aligning environmental difficulty with the Refiner{'}s evolving competence. ReQueR yields consistent absolute gains of 1.3{\%}{--}7.2{\%} across diverse architectures and benchmarks, outperforming strong baselines by 2.1{\%} on average. Crucially, it provides a promising paradigm for one-to-many inference-time reasoning elicitation, enabling a single Refiner trained on a small set of models to effectively unlock reasoning in diverse unseen Solvers. Code is available at https://github.com/newera-xiao/ReQueR."
}