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 fine-grained annotations. An LLM is then fine-tuned on these compressed pairs via a mixed-ratio supervised fine-tuning (SFT), teaching it to follow a spectrum of compression budgets and providing a stable initialization for reinforcement learning (RL). We further propose Constrained and Hierarchical Ratio Policy Optimization (CHRPO) to explicitly incentivize question-solving ability under lower budgets by a hierarchical reward. Experiments on three mathematical reasoning benchmarks show the superiority of Extra-CoT. For example, on MATH-500 using Qwen3-1.7B, Extra-CoT achieves over 73\% token reduction with an accuracy improvement of 0.6\%, significantly outperforming state-of-the-art (SOTA) methods. Our source codes have been released at https://github.com/Mwie1024/Extra-CoT.
Reinforcement Learning for Chain of Thought Compression with One-Domain-to-All Generalization
CFCS, School of Computer Science, Peking University（计算机学院，北京大学）；State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）；LLM-Core Xiaomi（小米LLM-Core）
GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders
Computer, Electrical and Mathematical Sciences and Engineering Division, KAUST（卡奥斯特大学计算机、电气与数学科学与工程分校）；Department of Informatics and Telecommunications, University of Peloponnese（希腊皮埃尼亚斯大学信息与电信系）；Physical Science and Engineering Division, KAUST（卡奥斯特大学物理科学与工程分校）
Training with Quantization Noise for Extreme Model Compression
Facebook AI Research（Facebook人工智能研究院）；LORIA（洛里亚计算机科学实验室）；Univ Lyon（里昂大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；ENS de Lyon（里昂高等师范学校）；UCB Lyon 1（里昂第一大学）；LIP UMR 5668（里昂第一大学计算机科学实验室）
The Spectral Geometry of Thought: Phase Transitions, Instruction Reversal, Token-Level Dynamics, and Perfect Correctness Prediction in How Transformers Reason
Crystal-KV: Efficient KV Cache Management for Chain-of-Thought LLMs via Answer-First Principle
School of Computer Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究所，合肥综合性国家科学中心）；Suzhou Institute for Advanced Research, University of Science and Technology of China（苏州先进研究院，中国科学技术大学）
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks
Multiverse Computing（多维计算公司）；Catalan Institute of Nanoscience and Nanotechnology (ICN2)（加泰罗尼亚纳米科学与纳米技术研究所）；The Barcelona Institute of Science and Technology（巴塞罗那科学技术研究院）；Donostia International Physics Center（多斯蒂亚国际物理中心）；Centre for Social Innovation（社会创新中心）
