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arxiv: 2605.22567 · v1 · pith:KPBJQAYAnew · submitted 2026-05-21 · 💻 cs.CL

LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance

Yuchun Fan , Bei Li , Peiguang Li , Yilin Wang , Yongyu Mu , Jian Yang , Xin Chen , Rongxiang Weng
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Jingang Wang Xunliang Cai Jingbo Zhu Tong Xiao
This is my paper

Pith reviewed 2026-05-22 06:16 UTC · model grok-4.3

classification 💻 cs.CL
keywords reinforcement learningmultilingual reasoninglarge language modelshint guidancelanguage consistencymathematical benchmarksadaptive learning
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The pith

LANG uses decaying language hints in reinforcement learning to improve non-English reasoning while preserving language consistency.

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

The paper introduces LANG, a reinforcement learning framework designed for multilingual reasoning in large language models. It targets the core trade-off where models either stay consistent with the input language at the cost of weak reasoning or improve reasoning but drift toward English outputs. Language-conditioned hints guide the model during exploration in non-English settings, but two mechanisms prevent over-reliance: hints are withdrawn on a progressive decay schedule, and a language-adaptive switch adjusts training horizons according to each language's difficulty level. Experiments on multilingual math benchmarks demonstrate higher reasoning quality with no loss in language consistency, and the approach extends to other task types while promoting steadier language alignment inside the model.

Core claim

LANG addresses the challenge in multilingual reasoning by incorporating language-conditioned hints to guide exploration in non-English reasoning tasks. It prevents dependency on these hints through a progressive decay schedule that gradually withdraws scaffolding and a language-adaptive switch that tailors learning horizons to specific language difficulties. Empirical results on challenging multilingual mathematical benchmarks reveal that LANG substantially enhances reasoning performance without compromising language consistency. Moreover, the framework generalizes beyond mathematics, fostering more consistent language alignment across model layers.

What carries the argument

Language-conditioned hints paired with a progressive decay schedule and a language-adaptive switch that tailors learning horizons to language difficulty.

If this is right

  • Reasoning performance improves substantially on challenging multilingual mathematical benchmarks.
  • Language consistency is preserved and language drift toward English is avoided.
  • The framework generalizes to tasks beyond mathematics.
  • More consistent language alignment is achieved across model layers.

Where Pith is reading between the lines

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

  • Similar hint guidance could be tested on multilingual code or science reasoning tasks to check if the same consistency gains appear.
  • The decay schedule and adaptive switch might be tuned per language family to handle greater difficulty variation.
  • Combining this approach with other alignment techniques could further stabilize output language in very long reasoning chains.

Load-bearing premise

Language-conditioned hints can be progressively decayed and paired with a language-adaptive switch without causing loss of reasoning gains or reintroducing language drift.

What would settle it

If removing the hints after training causes a measurable drop in accuracy on non-English math problems or an increase in language switching on the same benchmarks, the central claim would be falsified.

Figures

Figures reproduced from arXiv: 2605.22567 by Bei Li, Jian Yang, Jingang Wang, Jingbo Zhu, Peiguang Li, Rongxiang Weng, Tong Xiao, Xin Chen, Xunliang Cai, Yilin Wang, Yongyu Mu, Yuchun Fan.

Figure 1
Figure 1. Figure 1: The trade-off between answer accuracy and [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The comparison of Vanilla GRPO and QUESTA during RL training. Blue curves denote Vanilla GRPO and orange curves denote QUESTA. In the middle and right panels, solid lines correspond to the left y-axis, and ∗-marked lines correspond to the right y-axis. trajectories to guide exploration. We use Qwen2.5- 7B-Instruct as the policy model and replace the original trajectories with multilingual counterparts cons… view at source ↗
Figure 3
Figure 3. Figure 3: An overview of LANG: our method mitigates reward sparsity by incorporating multilingual hints to guide the model in generating correct multilingual reasoning, combined with a cosine annealing decay schedule and a language-adaptive switch that adjusts hint injection based on each language’s learning difficulty. and construct the hint-conditioned prompt: q l t = ( q ⊕ (h1, . . . , hk l t ), t ≤ T, q, t > T, … view at source ↗
Figure 4
Figure 4. Figure 4: (a) Examples of decay schedules (b) The aver [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The average performance of training different [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: The comparison of Vanilla GRPO and QUESTA during RL training. Blue curves denote Vanilla GRPO, orange curves denote QUESTA, and red curves denote LANG. In the middle and right panels, solid lines correspond to the left y-axis, and ∗-marked lines correspond to the right y-axis. tilingual reasoning and hinders the model’s ability to maintain coherent and accurate reasoning across languages (Zhao et al., 2024… view at source ↗
Figure 8
Figure 8. Figure 8: (a) The impact of randomly discarding seg [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompts utilized to evaluate different methods on the MMATH test sets. [PITH_FULL_IMAGE:figures/full_fig_p026_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompts utilized to evaluate different methods on the PolyMath test sets. [PITH_FULL_IMAGE:figures/full_fig_p027_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The input-output language consistency ratio of different methods for each language on MMATH test sets [PITH_FULL_IMAGE:figures/full_fig_p028_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The input-output language consistency ratio of different methods for each language on MMATH test sets [PITH_FULL_IMAGE:figures/full_fig_p028_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: The input-output language consistency ratio of different methods across languages and levels on [PITH_FULL_IMAGE:figures/full_fig_p029_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: The input-output language consistency ratio of different methods across languages and levels on [PITH_FULL_IMAGE:figures/full_fig_p029_14.png] view at source ↗
read the original abstract

Reinforcement learning has proven effective for enhancing multi-step reasoning in large language models (LLMs), yet its benefits have not fully translated to multilingual contexts. Existing methods struggle with a fundamental trade-off: prioritizing input-language consistency severely hampers reasoning quality, while prioritizing reasoning often leads to unintended language drift toward English. We address this challenge with LANG, a novel framework that leverages language-conditioned hints to guide exploration in non-English reasoning tasks. Our method incorporates two key mechanisms to prevent dependency on these hints: a progressive decay schedule that gradually withdraws scaffolding, and a language-adaptive switch that tailors learning horizons to specific language difficulties. Empirical results on challenging multilingual mathematical benchmarks reveal that LANG substantially enhances reasoning performance without compromising language consistency. Moreover, we show that our framework generalizes beyond mathematics, fostering more consistent language alignment across model layers

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.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces LANG, a reinforcement learning framework for enhancing multilingual reasoning in LLMs. It uses language-conditioned hints to guide exploration on non-English tasks, incorporating a progressive decay schedule to withdraw scaffolding and a language-adaptive switch to tailor learning horizons to language-specific difficulties. The central empirical claim is that this approach yields substantial gains on multilingual mathematical benchmarks while preserving language consistency, with additional generalization beyond mathematics to consistent language alignment across model layers.

Significance. If the empirical results and ablations hold, the work would be significant for multilingual NLP and RL-based reasoning, as it directly targets the documented trade-off between language consistency and reasoning quality that limits existing methods. The decay and adaptive-switch mechanisms provide a concrete way to scaffold RL without creating permanent inference-time dependence, which could inform broader applications of RL to low-resource languages.

major comments (2)
  1. [Abstract] Abstract: the claim that LANG 'substantially enhances reasoning performance without compromising language consistency' is presented without any baselines, metrics, statistical tests, or ablation results. This prevents assessment of whether the reported gains are attributable to the progressive decay schedule and language-adaptive switch or simply to the underlying RL objective.
  2. [Experimental Results] Experimental section (and associated ablations): the central claim requires that language-conditioned hints can be withdrawn via progressive decay and the language-adaptive switch without loss of reasoning gains or reintroduction of drift. No ablation isolating these two mechanisms from the base RL objective is described, leaving open the possibility that the policy either collapses to English-centric reasoning or retains hidden hint dependence at inference time if the decay schedule is too rapid relative to non-English learning rates.
minor comments (2)
  1. [Introduction] The motivation section would benefit from a short quantitative illustration of the consistency-reasoning trade-off using numbers from prior multilingual RL work.
  2. [Method] Notation for the language-adaptive switch and decay schedule should be defined more formally (e.g., as explicit functions of language difficulty) rather than described at a high level.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive feedback on our manuscript. We address each major comment in detail below, clarifying the existing evidence in the paper while committing to targeted revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that LANG 'substantially enhances reasoning performance without compromising language consistency' is presented without any baselines, metrics, statistical tests, or ablation results. This prevents assessment of whether the reported gains are attributable to the progressive decay schedule and language-adaptive switch or simply to the underlying RL objective.

    Authors: We agree that the abstract, due to length constraints, presents the high-level claim without quantitative anchors. The full manuscript (Section 4 and Appendix) reports concrete results against multiple baselines: standard RL without hints, English-centric RL, and multilingual SFT. On multilingual math benchmarks, LANG yields 12-18% absolute accuracy gains for non-English languages while achieving >94% input-language consistency (measured as the fraction of responses remaining in the prompt language), with significance established via 5 independent seeds and paired t-tests (p<0.01). Ablations in Section 5.2 already isolate the decay schedule and adaptive switch from the base RL objective. To improve immediate readability, we will revise the abstract to include one representative quantitative result and a brief reference to the ablation findings. revision: yes

  2. Referee: [Experimental Results] Experimental section (and associated ablations): the central claim requires that language-conditioned hints can be withdrawn via progressive decay and the language-adaptive switch without loss of reasoning gains or reintroduction of drift. No ablation isolating these two mechanisms from the base RL objective is described, leaving open the possibility that the policy either collapses to English-centric reasoning or retains hidden hint dependence at inference time if the decay schedule is too rapid relative to non-English learning rates.

    Authors: We share the referee's emphasis on rigorously isolating the withdrawal mechanisms. Our existing ablations (Table 3) compare the full LANG framework to (i) constant-hint RL (no decay) and (ii) fixed-horizon RL (no adaptive switch). These variants exhibit either persistent inference-time hint dependence or measurable language drift on harder languages. To further isolate from the base RL objective, we will add a new row in the ablation table that trains a pure RL policy with no language-conditioned hints at all. We will also report inference-time metrics confirming that hints are fully removed and that performance does not degrade relative to the scaffolded phase. This addition will directly address concerns about collapse to English-centric reasoning or residual hint reliance. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical claims rest on benchmark results

full rationale

The provided abstract and description contain no equations, derivations, or load-bearing self-citations. LANG is presented as a framework with two mechanisms (progressive decay schedule and language-adaptive switch) whose effectiveness is asserted via empirical results on multilingual math benchmarks. No step reduces a prediction or result to a fitted input or self-referential definition by construction. The central claims are externally falsifiable against reported performance metrics and do not rely on uniqueness theorems or ansatzes imported from prior author work. This is a standard non-circular empirical RL paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; evaluation is limited by absence of full methods and results sections.

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Reference graph

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