Crosslingual On-Policy Self-Distillation for Multilingual Reasoning

Hugging Face Daily Papers Papers
self-distillation multilingual mathematical-reasoning low-resource-languages crosslingual

Summary

The paper proposes Crosslingual On-Policy Self-Distillation (COPSD), a method to transfer high-resource language reasoning capabilities to low-resource languages using a shared student-teacher architecture. Experiments across 17 African languages show significant improvements in mathematical reasoning and answer-format adherence, outperforming Group Relative Policy Optimization (GRPO).

Large language models (LLMs) have achieved remarkable progress in mathematical reasoning, but this ability is not equally accessible across languages. Especially low-resource languages exhibit much lower reasoning performance. To address this, we propose Crosslingual On-Policy Self-Distillation (COPSD), which transfers a model's own high-resource reasoning behavior to low-resource languages. COPSD uses the same model as student and teacher: the student sees only the low-resource problem, while the teacher receives privileged crosslingual context, including the problem translation and reference solution in English. Training minimizes full-distribution token-level divergence on the student's own rollouts, providing dense supervision while avoiding the sparsity and instability of outcome-only reinforcement learning (RL). Experiments on 17 low-resource African languages show that COPSD consistently improves low-resource mathematical reasoning across model sizes and substantially outperforms Group Relative Policy Optimization (GRPO). Further analyses show that COPSD improves answer-format adherence, strengthens test-time scaling, and generalizes to harder multilingual reasoning benchmarks, with especially large gains for lower-resource languages. We make our code and data available at: https://github.com/cisnlp/COPSD.
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Paper page - Crosslingual On-Policy Self-Distillation for Multilingual Reasoning

Source: https://huggingface.co/papers/2605.09548

Abstract

COPSD transfers high-resource language model reasoning behavior to low-resource languages using self-distillation with crosslingual context, improving mathematical reasoning performance.

Large language models(LLMs) have achieved remarkable progress inmathematical reasoning, but this ability is not equally accessible across languages. Especiallylow-resource languagesexhibit much lower reasoning performance. To address this, we proposeCrosslingual On-Policy Self-Distillation(COPSD), which transfers a model’s own high-resource reasoning behavior tolow-resource languages. COPSD uses the same model as student and teacher: the student sees only the low-resource problem, while the teacher receives privileged crosslingual context, including the problem translation and reference solution in English. Training minimizes full-distributiontoken-level divergenceon the student’s own rollouts, providing dense supervision while avoiding the sparsity and instability of outcome-onlyreinforcement learning(RL). Experiments on 17 low-resource African languages show that COPSD consistently improves low-resourcemathematical reasoningacross model sizes and substantially outperforms Group RelativePolicy Optimization(GRPO). Further analyses show that COPSD improves answer-format adherence, strengthenstest-time scaling, and generalizes to hardermultilingual reasoning benchmarks, with especially large gains for lower-resource languages. We make our code and data available at: https://github.com/cisnlp/COPSD.

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