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Progressive code-switching transfers English reasoning to other languages using only lightweight translation and a curriculum.

2026-07-02 13:26 UTC pith:ZPPZEHRA

load-bearing objection PCS provides an efficient path for multilingual reasoning transfer using progressive code-switching after partial SFT, though the RL curriculum lacks an explicit correctness signal which could undermine accuracy claims. the 1 major comments →

arxiv 2607.00485 v1 pith:ZPPZEHRA submitted 2026-07-01 cs.CL

Efficient Multilingual Reasoning Transfer via Progressive Code-Switching

Zhijun Wang , Junxiao Liu , Hao Zhou , Hao-Ran Wei , Baosong Yang , Shujian Huang This is my paper
classification cs.CL
keywords multilingual reasoningcode-switchingreasoning transferreinforcement learningcurriculum learninglanguage consistencylarge reasoning models
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Large reasoning models perform well in English but lose much of that ability when asked to reason in other languages. This paper introduces Progressive Code-Switching to move the ability across languages without distillation from stronger models or external judges. The approach first builds mixed-language traces by translating only some English reasoning steps, then fine-tunes the model on them to begin code-switching. Reinforcement learning follows with a curriculum that steadily raises the share of target-language steps until the model reasons fully in the target language. Experiments on five typologically diverse languages show the method narrows the gap to English performance while producing more consistent target-language outputs.

Core claim

PCS first constructs code-switched reasoning traces by translating a subset of English reasoning steps into the target language and uses them to initialize the model's code-switching ability via supervised fine-tuning. It then applies reinforcement learning with a step-level language consistency curriculum, progressively raising the target-language ratio until the model reasons entirely in the target language. This progressive design provides a smooth transfer path that avoids the instability and performance degradation commonly observed when directly enforcing target-language reasoning.

What carries the argument

Progressive Code-Switching (PCS) that initializes via partial-translation code-switched traces and then applies a reinforcement-learning curriculum on step-level language consistency.

Load-bearing premise

That constructing code-switched traces by translating only a subset of English reasoning steps provides a stable initialization for the model's code-switching ability that the subsequent RL curriculum can build upon without external supervision.

What would settle it

If training directly on fully translated target-language traces without the progressive curriculum produces equal or better accuracy and language consistency, the benefit of the gradual shift would be in doubt.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The performance gap between target-language and English reasoning narrows substantially on multiple benchmarks.
  • Reasoning becomes more consistent in the target language across steps.
  • Accuracy stays competitive for five typologically diverse languages.
  • Transfer succeeds without any stronger model for distillation or judging.

Where Pith is reading between the lines

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

  • The same gradual curriculum idea might stabilize transfer of other skills such as instruction following or tool use across languages.
  • The approach could reduce reliance on high-quality native-language data when deploying reasoning models in new languages.
  • Applying the method to models of different sizes or to additional languages would test how far the stability benefit extends.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 1 minor

Summary. The paper proposes Progressive Code-Switching (PCS) as an efficient framework to transfer English reasoning capabilities of large reasoning models to target languages. PCS constructs code-switched traces by translating only a subset of English reasoning steps, initializes via supervised fine-tuning, and then applies reinforcement learning driven by a step-level language consistency curriculum that progressively increases the target-language ratio until the model reasons fully in the target language. Experiments across multiple benchmarks and five typologically diverse languages are reported to show that PCS narrows the target-language vs. English performance gap while producing more language-consistent outputs and maintaining competitive accuracy, all without distillation from stronger models or external judges.

Significance. If the central results hold under scrutiny, the work would be significant because it removes the need for stronger models in the transfer pipeline, offering a lighter-weight path to multilingual reasoning that could scale more readily than distillation-based methods. The progressive curriculum is positioned as the mechanism that stabilizes the transfer and avoids the degradation seen in direct target-language enforcement.

major comments (1)
  1. [Abstract] Abstract (method description): the RL stage is described as using only a 'step-level language consistency curriculum' whose sole objective is raising the target-language ratio. No explicit correctness, validity, or outcome reward is mentioned. This is load-bearing for the central claim that accuracy is preserved once the ratio reaches 1.0; without such a term, nothing in the stated procedure prevents the policy from converging to fluent but logically invalid chains after English scaffolding is removed. An ablation or analysis showing that language-consistency alone suffices to retain validity is required.
minor comments (1)
  1. The abstract refers to 'multiple benchmarks' without naming them; the introduction or experimental section should list the exact datasets and metrics at the outset.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the thoughtful and detailed review. The concern about the RL objective description is well-taken and points to a place where the abstract could be more precise. We address it directly below and commit to revisions that clarify the method and strengthen the supporting analysis.

read point-by-point responses

  1. Referee: [Abstract] Abstract (method description): the RL stage is described as using only a 'step-level language consistency curriculum' whose sole objective is raising the target-language ratio. No explicit correctness, validity, or outcome reward is mentioned. This is load-bearing for the central claim that accuracy is preserved once the ratio reaches 1.0; without such a term, nothing in the stated procedure prevents the policy from converging to fluent but logically invalid chains after English scaffolding is removed. An ablation or analysis showing that language-consistency alone suffices to retain validity is required.

    Authors: We agree that the abstract's phrasing is concise and does not explicitly reference any correctness or outcome-based reward term. The RL component is driven by a step-level language-consistency reward whose objective is to increase the target-language ratio according to the curriculum schedule. The manuscript argues that validity is retained because (1) the policy is initialized via SFT on code-switched traces that preserve English reasoning structure and (2) the progressive schedule avoids the abrupt removal of English scaffolding that produces degradation in direct-enforcement baselines (reported in Section 4.3). Nevertheless, the referee correctly identifies that an explicit ablation isolating the contribution of language consistency to validity would make this claim more robust. We will add such an analysis in the revision, including a comparison of reasoning validity metrics (e.g., step-level logical consistency checks) between the progressive curriculum and a non-progressive language-consistency baseline. revision: yes

Circularity Check

0 steps flagged

No circularity detected in derivation or claims

full rationale

The paper describes a procedural method (partial translation for SFT initialization followed by RL on language-consistency curriculum) and reports experimental narrowing of performance gaps on benchmarks. No equations, parameter fits, self-citations, or uniqueness theorems are present in the provided text that would reduce any claimed result to a definition, renaming, or construction by the inputs themselves. The central claims rest on external experimental outcomes rather than any self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented entities are stated. Ledger left empty pending full text.

pith-pipeline@v0.9.1-grok · 5748 in / 1089 out tokens · 20452 ms · 2026-07-02T13:26:53.928283+00:00 · methodology

0 comments
read the original abstract

Large reasoning models (LRMs) have achieved strong reasoning capabilities in English, yet their performance degrades significantly when required to reason in other languages. A natural solution is to transfer the model's English reasoning ability to target languages. However, existing transfer approaches typically rely on distilled target-language reasoning traces from stronger LRMs or online supervision from external judge models, which are costly and difficult to scale. In this paper, we propose PCS (Progressive Code-Switching), a more efficient transfer framework that requires only lightweight translation without any stronger model for distillation or judging. PCS first constructs code-switched reasoning traces by translating a subset of English reasoning steps into the target language, and uses them to initialize the model's code-switching ability via supervised fine-tuning. It then applies reinforcement learning with a step-level language consistency curriculum, progressively raising the target-language ratio until the model reasons entirely in the target language. This progressive design provides a smooth transfer path that avoids the instability and performance degradation commonly observed when directly enforcing target-language reasoning. Experiments on multiple benchmarks and five typologically diverse languages show that PCS substantially narrows the performance gap between target-language and English reasoning, yielding more language-consistent reasoning while maintaining competitive accuracy.

Figures

Figures reproduced from arXiv: 2607.00485 by Baosong Yang, Hao-Ran Wei, Hao Zhou, Junxiao Liu, Shujian Huang, Zhijun Wang.

Figure 1
Figure 1. Figure 1: The illustration of Progressive Code-Switching. Starting from a cold-start model, PCS progressively increases the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The Framework of PCS. • Accuracy reward (racc): racc = 1 if the answer is correct, otherwise 0. • Step-level language consistency reward (rSLC): We ap￾ply regular expressions to remove mathematical content. Then use langdetect1 to identify the language of each step. rSLC = 1 if SLC(T, L) ≥ τ , otherwise 0. As demonstrated in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Part of M-Thinker’s thinking process. The SoftLC RL and M-Thinker baselines both use response-level language consistency rewards, which reduces language identification to a binary decision based on off￾the-shelf language detection tools. In our experiments, this design is largely effective for SoftLC RL. However, the same response-level constraint becomes ineffective for M-Thinker. As shown in [PITH_FULL_… view at source ↗
Figure 5
Figure 5. Figure 5: The training curves for SLC(T, L) and Acc for PCS, PCS-Dense, and SoftLC RL. We attribute this to the inherently gradual nature of cross￾lingual transfer via code-switched reasoning. Because RL is optimized on mini-batches, satisfying the language objec￾tive on the current batch does not necessarily generalize to other samples. In contrast, dense rewards push the model to optimize language consistency too … view at source ↗
Figure 4
Figure 4. Figure 4: Training curves for SLC(T, L), τ , and Acc of PCS [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: The SLC&Acc results of MMATH across different [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The MEXA multilingual alignment score of [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: The ablation results for PCS and PCS without [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 8
Figure 8. Figure 8: Instructions used for Prompt Control Statistics FR PT JA KO TH LC(%) 99.1 98.8 98.7 96.7 99.3 CS Ratio(%) 0.7 1.6 1.2 1.2 1.5 [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 11
Figure 11. Figure 11: Case Study of PCS’s model for one sampled Thai [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages · 1 internal anchor

  1. [1]

    Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training

    Wang, Zhijun and Li, Jiahuan and Zhou, Hao and Weng, Rongxiang and Wang, Jingang and Huang, Xin and Han, Xue and Feng, Junlan and Deng, Chao and Huang, Shujian. Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training. Findings of the Association for Computational Linguistics: ACL 2025. 2025. doi:10.18653/v1/2025.findings-acl.575

    work page doi:10.18653/v1/2025.findings-acl.575 2025

  2. [2]

    2024 , eprint=

    DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models , author=. 2024 , eprint=

    work page 2024

  3. [3]

    Guo, Daya and Yang, Dejian and Zhang, Haowei and Song, Junxiao and Wang, Peiyi and Zhu, Qihao and Xu, Runxin and Zhang, Ruoyu and Ma, Shirong and Bi, Xiao and Zhang, Xiaokang and Yu, Xingkai and Wu, Yu and Wu, Z. F. and Gou, Zhibin and Shao, Zhihong and Li, Zhuoshu and Gao, Ziyi and Liu, Aixin and Xue, Bing and Wang, Bingxuan and Wu, Bochao and Feng, Bei ...

    work page doi:10.1038/s41586-025-09422-z

  4. [4]

    2025 , eprint=

    Qwen3 Technical Report , author=. 2025 , eprint=

    work page 2025

  5. [5]

    2024 , eprint=

    OpenAI o1 System Card , author=. 2024 , eprint=

    work page 2024

  6. [6]

    2025 , eprint=

    Language Matters: How Do Multilingual Input and Reasoning Paths Affect Large Reasoning Models? , author=. 2025 , eprint=

    work page 2025

  7. [7]

    2025 , eprint=

    MMATH: A Multilingual Benchmark for Mathematical Reasoning , author=. 2025 , eprint=

    work page 2025

  8. [8]

    2025 , eprint=

    Magistral , author=. 2025 , eprint=

    work page 2025

  9. [9]

    2025 , eprint=

    PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts , author=. 2025 , eprint=

    work page 2025

  10. [10]

    2025 , eprint=

    AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale , author=. 2025 , eprint=

    work page 2025

  11. [11]

    2026 , eprint=

    Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning , author=. 2026 , eprint=

    work page 2026

  12. [12]

    Latino Discourse and Communicative Behavior/Ablex Publishing , year=

    Syntactic Structure and Social Function of Code Switching , author=. Latino Discourse and Communicative Behavior/Ablex Publishing , year=

    work page

  13. [13]

    P re A lign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment

    Li, Jiahuan and Huang, Shujian and Ching, Aarron and Dai, Xinyu and Chen, Jiajun. P re A lign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024. doi:10.18653/v1/2024.emnlp-main.572

    work page doi:10.18653/v1/2024.emnlp-main.572 2024

  14. [14]

    arXiv preprint arXiv:2411.02460 , year=

    Code-Switching Curriculum Learning for Multilingual Transfer in LLMs , author=. arXiv preprint arXiv:2411.02460 , year=

    work page arXiv

  15. [15]

    Mapo: Advancing multilingual reasoning through multilingual alignment-as-preference optimization

    Mapo: Advancing multilingual reasoning through multilingual alignment-as-preference optimization , author=. arXiv preprint arXiv:2401.06838 , year=

    work page arXiv

  16. [16]

    Think natively: Unlocking multilingual reasoning with consistency- enhanced reinforcement learning, 2026

    Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning , author=. arXiv preprint arXiv:2510.07300 , year=

    work page arXiv

  17. [17]

    mcot: Multilingual instruction tuning for reasoning consistency in language models

    mCoT: Multilingual instruction tuning for reasoning consistency in language models , author=. arXiv preprint arXiv:2406.02301 , year=

    work page arXiv

  18. [18]

    S o T : Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models

    Qi, Rui and Man, Zhibo and Chen, Yufeng and Mo, Fengran and Xu, Jinan and Huang, Kaiyu. S o T : Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025. doi:10.18653/v1/2025.findings-emnlp.586

    work page doi:10.18653/v1/2025.findings-emnlp.586 2025

  19. [19]

    arXiv preprint arXiv:2511.23231 , year=

    Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering , author=. arXiv preprint arXiv:2511.23231 , year=

    work page arXiv

  20. [20]

    When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy

    Qi, Jirui and Chen, Shan and Xiong, Zidi and Fern \'a ndez, Raquel and Bitterman, Danielle and Bisazza, Arianna. When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025. doi:10.18653/v1/2025.findings-emnlp.1103

    work page doi:10.18653/v1/2025.findings-emnlp.1103 2025

  21. [21]

    2025 , eprint=

    MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment , author=. 2025 , eprint=

    work page 2025

  22. [22]

    2024 , eprint=

    Do Llamas Work in English? On the Latent Language of Multilingual Transformers , author=. 2024 , eprint=

    work page 2024

  23. [23]

    2026 , eprint=

    TranslateGemma Technical Report , author=. 2026 , eprint=

    work page 2026

  24. [24]

    2025 , eprint=

    MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation , author=. 2025 , eprint=

    work page 2025

  25. [25]

    arXiv preprint arXiv:2510.04230 , year=

    Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought , author=. arXiv preprint arXiv:2510.04230 , year=

    work page arXiv

  26. [26]

    CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning

    CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning , author=. arXiv preprint arXiv:2601.13262 , year=

    work page internal anchor Pith review Pith/arXiv arXiv