Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression
Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computational overhead for inference. Existing CoT compression methods often suffer from a critical loss of logical fidelity at high compression ratios, resulting in significant performance degradation. To achieve high-fidelity, fast reasoning, we propose a novel EXTreme-RAtio Chain-of-Thought Compression framework, termed Extra-CoT, which aggressively reduces the token budget while preserving answer accuracy. To generate reliable, high-fidelity supervision, we first train a dedicated semantically-preserved compressor on mathematical CoT data with fi...
| Authors | Yuntian Tang, Bohan Jia, Wenxuan Huang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 9 February 2026 |
| Last updated at source | 12 June 2026 |
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