AI RESEARCH
Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression
arXiv CS.LG
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ArXi:2602.08324v3 Announce Type: replace 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.