Multilingual Chain-of-Thought Compression via Cross-Lingual Distillation

2026-06-26 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Jiarui Wan, Songming Zhang, and Yufeng Chen propose Multilingual Chain-of-thought Compression via Cross-lingual Distillation (MCD), a novel framework addressing the verbosity and inconsistent performance of Chain-of-Thought (CoT) reasoning in large language models, particularly in multilingual environments. CoT outputs often increase inference costs, a problem exacerbated by varying tokenization and linguistic structures across languages, leading to accuracy degradation in low-resource languages with existing English-centric methods. MCD tackles this through a two-pronged approach: constructing a cross-lingually aligned dataset using a translation-with-verification pipeline and difficulty-aware sampling, and employing a reinforcement training strategy that combines supervised fine-tuning with direct preference optimization. Experiments on multilingual mathematical benchmarks demonstrate that MCD consistently reduces reasoning length while maintaining competitive accuracy and significantly improves robustness in low-resource languages. This work was presented at the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026) in July 2026.

Key takeaway

For NLP Engineers optimizing large language model inference costs in multilingual applications, you should consider implementing cross-lingual distillation techniques like MCD. This approach can significantly reduce Chain-of-Thought reasoning length while maintaining accuracy, especially improving robustness in low-resource languages. Integrating such methods into your LLM deployment strategy will enhance efficiency and broaden language support without sacrificing performance.

Key insights

MCD compresses multilingual Chain-of-Thought reasoning using cross-lingual data alignment and reinforcement learning, improving efficiency and low-resource language robustness.

Principles

• Cross-lingual data alignment improves multilingual CoT.
• Reinforcement training optimizes reasoning conciseness.
• Difficulty-aware sampling enhances dataset quality.

Method

MCD builds a cross-lingually aligned dataset via translation-with-verification and difficulty-aware sampling, then applies reinforcement training combining supervised fine-tuning with direct preference optimization.

In practice

• Reduce LLM inference costs in multilingual CoT.
• Improve CoT accuracy for low-resource languages.
• Enhance robustness of multilingual reasoning.

Topics

• Multilingual LLMs
• Chain-of-Thought
• Model Compression
• Cross-lingual Distillation
• Reinforcement Learning
• Low-Resource Languages

Best for: AI Engineer, Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.