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Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning

Paper · 2025 · Guijin Son, Jiwoo Hong, Hyunwoo Ko et al.

Summary

The paper asks whether test-time scaling methods, which spend extra compute at inference, help mathematical reasoning across many languages as they do in English. Existing multilingual math tests such as MGSM are close to saturated: o3-mini scores 89.30. The authors therefore built MCLM (Multilingual Competition Level Math), a competition-level benchmark covering 55 languages. It combines GPT-4o translations of MATH-500 and AIME problems with human-written or officially translated Olympiad questions.

They compared three strategies under matched inference FLOPs:

  • Outcome Reward Modeling (ORM): best-of-N selection.
  • Process Reward Modeling (PRM): step-level guidance during generation.
  • Budget Forcing (BF): controlling the length of a thinking model's reasoning.

The generators were Qwen2.5-Math models and MR1-1.5B, a new multilingual reasoning model. With equal compute, the three methods perform about the same. Gains are large in English but small and uneven in other languages. Extra compute can also make results less consistent across languages. The work shows that test-time scaling does not automatically transfer across languages. It also supplies an open, harder multilingual evaluation resource.

Key points

  • MCLM has four subsets:
    • MT-MATH100 (55 languages, 100 items) and MT-AIME2024 (55 languages, 30 items) are graded by a rule-based verifier.
    • M-IMO (38 languages, 22–27 items) and M-MO (11 languages, 28–31 items) are graded by LLM-as-a-Judge using GPT-4o-mini.
  • ORM used Qwen2.5-Math-72B-RM and PRM used Qwen2.5-Math-72B-PRM. Compute budgets were aligned by estimating inference FLOPs.
  • Qwen2.5-1.5B Math with ORM scored 35.84 on MCLM, and MR1-1.5B with BF scored 35.23.
  • BF raised English AIME accuracy by about 20 points, but other languages gained only 1.94 points on average. Some languages, such as Latvian and Romanian, even declined.
  • MR1-1.5B was fine-tuned from Deepseek-R1-1.5B on 100k R1-distilled examples translated by GPT-4o. It roughly matches GPT-4o-Mini on multilingual math reasoning.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksInformation Systems · 1,730 worksMathematics · 189 worksDecision Sciences · 249 worksManagement Science and Operations Research · 134 worksApplied Mathematics · 52 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1,685 worksMultilingual & cross-lingual NLP · 882 works

Cite

Cite as Guijin Son, Jiwoo Hong, Hyunwoo Ko and James Thorne. 2025. Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning. Proceedings of the 63rd 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/v1/2025.acl-long.699

@inproceedings{son2025linguistic,
  title = {{Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning}},
  author = {Guijin Son, Jiwoo Hong, Hyunwoo Ko and James Thorne},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.699},
  url = {https://doi.org/10.18653/v1/2025.acl-long.699}
}

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