arxiv: 2604.25444 · v1 · submitted 2026-04-28 · 💻 cs.CL

Recognition: unknown

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

Yixiao Zhou , Dongzhou Cheng , Zhiliang Wu , Yi Yang , Yu Cheng , Hehe Fan

Authors on Pith no claims yet

Pith reviewed 2026-05-07 16:13 UTC · model grok-4.3

classification 💻 cs.CL

keywords inference-time reasoning elicitationquery refinementreinforcement learningLLM alignmentone-to-many generalizationadaptive solver hierarchy

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The pith

A single reinforcement-learned refiner can rewrite queries to unlock reasoning in many different frozen large language models at inference time.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models often hold latent reasoning skills that stay unused because human queries are ambiguous or unstructured. The paper trains one specialized Refiner policy with reinforcement learning to convert raw questions into explicit logical steps, treating the target LLMs as fixed environments rather than retraining them. An Adaptive Solver Hierarchy curriculum keeps training stable by matching task difficulty to the refiner's current skill. The resulting system delivers steady gains across model sizes and tasks and, crucially, transfers to models never seen during training. This matters because it offers a low-cost, one-time training route instead of expensive per-model fine-tuning for each new architecture.

Core claim

ReQueR trains a Refiner policy via reinforcement learning to rewrite ambiguous queries into explicit logical decompositions. Frozen LLMs serve as the environment, and the Adaptive Solver Hierarchy dynamically adjusts problem difficulty to the refiner's improving competence, drawing from the Zone of Proximal Development. Once trained on a small set of models, the same refiner produces consistent absolute improvements of 1.7 to 7.2 percent on diverse benchmarks and generalizes to unseen models, outperforming static prompts and per-model baselines by 2.1 percent on average.

What carries the argument

The Refiner policy inside ReQueR, trained by reinforcement learning to produce explicit logical decompositions of queries, with the Adaptive Solver Hierarchy acting as a curriculum that aligns environmental difficulty to the policy's competence.

If this is right

Consistent absolute gains of 1.7 to 7.2 percent appear across architectures and benchmarks.
The same refiner outperforms strong baselines by 2.1 percent on average.
One trained refiner supports one-to-many inference-time reasoning elicitation.
The refiner works on diverse models it never encountered during training.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The approach could lower the total compute needed to equip many LLMs with better reasoning by avoiding repeated fine-tuning runs.
Similar inference-time refinement policies might be trained for other latent capabilities such as factual grounding or step-by-step planning.
If the refiner's output format is kept model-agnostic, it could combine with existing prompting or decoding strategies without conflict.

Load-bearing premise

A single refiner policy trained on a limited set of models and tasks will generalize to improve reasoning in many unseen models without major performance drop or overfitting.

What would settle it

Train the refiner on one small group of models, then apply it to a new model architecture or task family never used in training; if performance stays flat or drops compared with the original queries, the generalization claim is falsified.

Figures

Figures reproduced from arXiv: 2604.25444 by Dongzhou Cheng, Hehe Fan, Yixiao Zhou, Yi Yang, Yu Cheng, Zhiliang Wu.

**Figure 1.** Figure 1: Eliciting latent reasoning via inference-time query refinement. A Solver struggles with the ambiguity of a raw human query (top). ReQueR functions as an optimization frontend, reformulating the input into a structured, explicit format (bottom). By resolving ambiguity and employing strategies like variable-mapping, the Refiner aligns the query with the Solver’s reasoning patterns, effectively unlocking i… view at source ↗

**Figure 2.** Figure 2: ReQueR reinforcement learning pipeline. The Refiner generates multiple refined queries for a Solver dynamically assigned by the ASH based on problem difficulty. After computing rewards from the Solver’s answers, the framework optimizes the Refiner policy and updates Solver labels to ensure a stable learning curriculum. while GEPA (Agrawal et al., 2025) employs genetic evolution. However, these methods are… view at source ↗

**Figure 3.** Figure 3: Evolution of Solver distribution during training. ASH dynamically matches Solver difficulty to the Refiner’s competence view at source ↗

**Figure 4.** Figure 4: Progress of validation accuracy during training. Both ASH and Leak Penalty are necessary for maintaining stable training and robust generalization. 4.3 Further Analysis Emergent Refinement Strategies RL training yields high-value strategies that underpin ReQueR’s effectiveness. As shown in view at source ↗

read the original 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 (\textbf{Re}inforcement \textbf{Que}ry \textbf{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 1.7\%--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 models. Code is available at https://github.com/newera-xiao/ReQueR.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ReQueR trains one RL refiner to rewrite queries for better LLM reasoning at inference time and claims it generalizes to unseen models, but the cross-model evidence looks thin.

read the letter

The paper's central pitch is that a single refiner policy, trained once with RL on a handful of models, can rewrite raw queries into logical decompositions and lift reasoning performance across many LLMs, including ones never seen in training. They add an Adaptive Solver Hierarchy that adjusts task difficulty on the fly, drawing from the Zone of Proximal Development idea, to keep training stable while the refiner improves. The reported gains are modest but consistent: 1.7-7.2% absolute across benchmarks and architectures, beating baselines by about 2.1% on average, with code released publicly.

Referee Report

3 major / 2 minor

Summary. The paper proposes ReQueR, a modular RL-based framework that trains a single Refiner policy to rewrite raw queries into explicit logical decompositions at inference time, treating frozen LLMs as the environment. It introduces the Adaptive Solver Hierarchy as a curriculum mechanism rooted in the Zone of Proximal Development to stabilize training. The central empirical claim is that this yields consistent absolute gains of 1.7%–7.2% across diverse architectures and benchmarks, outperforming strong baselines by 2.1% on average, while enabling one-to-many generalization: a Refiner trained on a small set of models effectively unlocks reasoning in diverse unseen models.

Significance. If the generalization result holds under rigorous cross-model controls, the work would represent a meaningful advance in inference-time alignment techniques. It offers a scalable alternative to per-model fine-tuning (avoiding O(N) costs) and moves beyond static prompts by learning query-level structural refinements via RL. The provision of code further supports potential reproducibility and follow-up work on inference-time reasoning elicitation.

major comments (3)

[Abstract and §4] Abstract and §4 (Results): The headline generalization claim—that a single Refiner trained on limited models unlocks reasoning in diverse unseen models—requires explicit evidence that the unseen models differ substantially in architecture, tokenizer, pre-training corpus, or scale from the training distribution. Without such controls, the reported gains may reflect overfitting to shared failure modes or output styles rather than learning architecture-invariant query decompositions.
[§3.2] §3.2 (Adaptive Solver Hierarchy): While the curriculum stabilizes training by aligning environmental difficulty with the Refiner's competence, it does not directly enforce or measure invariance of the learned policy to the specific reward signals produced by the training LLMs. This leaves open whether the policy gradient is shaped by idiosyncratic model behaviors, undermining the one-to-many transfer argument.
[§4] §4 (Experimental setup): The abstract reports specific percentage gains but the provided description lacks details on the exact baselines, number of runs, statistical significance tests, error bars, or the precise definition of 'unseen' models. These omissions make it impossible to verify whether the 2.1% average outperformance is robust or load-bearing for the central claim.

minor comments (2)

[Abstract] Abstract: The code link is a positive addition for reproducibility; ensure the repository includes the exact training configurations and evaluation scripts used for the reported numbers.
[§2] §2 (Related work): A brief comparison to prior inference-time prompt optimization or query-rewriting methods would help situate the contribution more precisely.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below with clarifications and commitments to revision where the concerns are valid and actionable.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (Results): The headline generalization claim—that a single Refiner trained on limited models unlocks reasoning in diverse unseen models—requires explicit evidence that the unseen models differ substantially in architecture, tokenizer, pre-training corpus, or scale from the training distribution. Without such controls, the reported gains may reflect overfitting to shared failure modes or output styles rather than learning architecture-invariant query decompositions.

Authors: We agree that the generalization claim would be strengthened by explicit documentation of differences between training and unseen models. The current manuscript describes the models as diverse but does not include a side-by-side comparison. In the revised version we will add a new table in §4 that reports architecture family, tokenizer vocabulary size and type, pre-training data sources, and parameter scale for the training models versus each unseen model. This addition will make the one-to-many transfer argument more rigorous. revision: yes
Referee: [§3.2] §3.2 (Adaptive Solver Hierarchy): While the curriculum stabilizes training by aligning environmental difficulty with the Refiner's competence, it does not directly enforce or measure invariance of the learned policy to the specific reward signals produced by the training LLMs. This leaves open whether the policy gradient is shaped by idiosyncratic model behaviors, undermining the one-to-many transfer argument.

Authors: The referee correctly identifies that the Adaptive Solver Hierarchy is a curriculum device for training stability and does not contain an explicit invariance regularizer with respect to reward signals. While the empirical transfer results provide supporting evidence, we will revise §3.2 to add a short discussion of this limitation and include a new ablation that evaluates the Refiner when reward signals are drawn from held-out models during training. This will directly test the degree of reward-signal invariance. revision: partial
Referee: [§4] §4 (Experimental setup): The abstract reports specific percentage gains but the provided description lacks details on the exact baselines, number of runs, statistical significance tests, error bars, or the precise definition of 'unseen' models. These omissions make it impossible to verify whether the 2.1% average outperformance is robust or load-bearing for the central claim.

Authors: We accept that the experimental section is insufficiently detailed for full verification. The revised manuscript will expand §4 to list all baselines with citations, state that all results are averaged over five independent runs with standard deviation error bars, report paired t-test p-values for the 2.1% average improvement, and provide an explicit definition of 'unseen' models together with the precise list of models used in each category. These changes will be reflected in the results tables and figures as well. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical RL training and evaluation

full rationale

The paper presents ReQueR as an RL-trained query refiner using frozen LLMs as the environment, with the Adaptive Solver Hierarchy as a curriculum for training stability. All reported gains (1.7%-7.2% absolute) and the one-to-many generalization claim are framed as outcomes of training on a small model set and testing on held-out architectures, with no equations, derivations, or first-principles predictions that reduce to the inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems, and no fitted parameters are relabeled as predictions. The method is self-contained against external benchmarks via direct experimentation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the method relies on standard RL concepts and the Zone of Proximal Development idea from psychology.

pith-pipeline@v0.9.0 · 5557 in / 1097 out tokens · 41148 ms · 2026-05-07T16:13:42.006671+00:00 · methodology

discussion (0)

Reference graph

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