连续的推理是否更适合多语言推理？
Is continuous CoT better suited for multi-lingual reasoning?
AI总结：
本文提出连续链式推理方法，证明其在多语言推理任务中优于传统方法，尤其在低资源语言和零样本设置中表现更优，同时显著提升推理效率。
摘要：
我们研究在连续潜在空间中进行推理是否能带来更稳健的多语言能力。我们比较了连续链式推理（使用CODI框架）与标准监督微调方法，涵盖五种语言类型各异的语言：英语、中文、德语、法语和乌尔都语。在GSM8k和CommonsenseQA上的实验表明，连续推理在低资源语言上显著优于显式推理，特别是在零样本设置中，目标语言在训练时未被见过。此外，这种方法实现了极高的效率，将推理轨迹压缩了约29倍到50倍。这些发现表明，连续潜在表示自然地表现出更大的语言不变性，为跨语言推理提供了可扩展的解决方案。
英文摘要：
We investigate whether performing reasoning in a continuous latent space leads to more robust multilingual capabilities. We compare Continuous Chain-of-Thought (using the CODI framework) against standard supervised fine-tuning across five typologically diverse languages: English, Chinese, German, French, and Urdu. Our experiments on GSM8k and CommonsenseQA demonstrate that continuous reasoning significantly outperforms explicit reasoning on low-resource languages, particularly in zero-shot settings where the target language was not seen during training. Additionally, this approach achieves extreme efficiency, compressing reasoning traces by approximately $29\times$ to $50\times$. These findings indicate that continuous latent representations naturally exhibit greater language invariance, offering a scalable solution for cross-lingual reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
