Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training
Large language models (LLMs) can now solve complex problems through long chain-of-thought (CoT) reasoning, but the trade-off between performance and token cost remains a central challenge. To address this issue, supervised fine-tuning (SFT) often uses compressed reasoning data, where CoT traces are shortened into compact forms. However, the effect of such compressed reasoning data on post-training remains poorly understood. In this paper, we propose a taxonomy of CoT consisting of Explicit CoT, which outputs all operations without aggregation, Composed CoT, which combines multiple operations into a single step, and Implicit CoT, which omits intermediate operations. As a controlled mechani...
| Authors | Kohsei Matsutani, Gouki Minegishi, Takeshi Kojima et al. |
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| Primary category | cs.AI |
| Published | 27 May 2026 |
| Last updated at source | 31 August 2026 |
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