Chain-of-thought reasoning improves the performance of large language models on complex tasks but often produces overly verbose outputs, leading to increased inference cost. This issue is exacerbated in multilingual settings, where differences in tokenization and linguistic structure result in inconsistent compression performance across languages. Existing methods are largely English-centric and tend to suffer from accuracy degradation, especially in low-resource languages.We propose Multilingual Chain-of-thought Compression via Cross-lingual Distillation (MCD), a unified framework that addresses these challenges through both data construction and optimization. MCD builds a cross-lingually aligned dataset using a translation-with-verification pipeline and difficulty-aware sampling, and employs a reinforcement training strategy that combines supervised fine-tuning with direct preference optimization to encourage concise yet sufficient reasoning.Experiments on multilingual mathematical benchmarks show that MCD consistently reduces reasoning length while maintaining competitive accuracy, and significantly improves robustness in low-resource languages.

Current large reasoning models (LRMs) prefer to generate verbose chain-of-thought (CoT) traces (a.k.a. the “overthinking” issue), especially in multilingual scenarios due to the imbalanced proficiency across different languages. However, existing studies on CoT compression focus exclusively on English and overlook language characteristics, resulting in performance degradation in multilingual scenarios. To address this issue, we develop a multilingual CoT compression method based on reinforcement learning, named Language-aware Entropy-based (LAE), which optimizes LLMs with multilingual group sampling and entropy gap-based rewards. Specifically, we extend GRPO with multilingual group sampling, where each query is jointly optimized across multiple languages. We further design a language-aware entropy-gap reward that penalizes redundant uncertainty in the thinking phase and aligns reasoning complexity across languages, enabling concise yet accurate multilingual CoT reasoning. Extensive experiments on the multilingual reasoning benchmarks MGSM, MMATH and MMMLU demonstrate that our method achieves competitive performance and generalization, compared with several strong baselines.