CoT Compression

CoT: Chain-of-Thought

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Quantifying the Stability of Multi-Step Reasoning via Error Amplification
Oct 5, 2026Dongyue Li, Ziniu Zhang, Minxuan Duan +1Efficient Language Model Reasoning CoT Compression
Northeastern University Boston, MA
On the Token Value Inequality in Efficient Reasoning
Sep 27, 2026Runjia Zeng, Hang Hua, Yiyang Liu +5RL for Language Model Reasoning Efficient Language Model Reasoning
Rochester Institute of Technology · Purdue University · MIT-IBM Watson AI Lab +3
When Can Agents Forget Their Reasoning? ICLR for Long-Horizon Agent Context Compression
Sep 24, 2026Mingxuan Wang, Fei Luo, Bo Wang +6Long-Context Modeling Long-Horizon LLM Agents
TierFlow Team · Gaoling School of Artificial Intelligence, Renmin University of China · Tsinghua University
A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM
Sep 7, 2026Xiaoang Xu, Siyuan Liu, Shuo Wang +13Efficient Language Model Reasoning Continuous Latent Reasoning
Beijing University of Posts and Telecommunications · OpenBMB · The Hong Kong Polytechnic University +2
Every Token Leaves a Ripple in the Stream of Thought: Eliciting Model-Internal Token Saliency for Chain-of-Thought Compression
Aug 31, 2026Tianyi Zhao, Yinhan He, Wendy Zheng +2LLM Compression Efficient Language Model Reasoning
University of Virginia
Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models
Jul 30, 2026Sara Candussio, Daniel Scalena, Luca Bortolussi +3CoT Compression Large Reasoning Models
University of Trieste · University of Milano-Bicocca · CLCG, University of Groningen +2
Efficient Chain-of-Modality Reasoning via Progressive Compression for Spoken Language Models
Jul 22, 2026Pengchao Feng, Chao-Hong Tan, Qian Chen +3Efficient Language Model Reasoning Speech Language Models
Shanghai Jiao Tong University · Shanghai Innovation Institute · Shanghai, China +2
Training Continuous Chain of Thought Models: A Tale of Two Regimes
Jul 18, 2026Varun Yerram, He He, Eunsol ChoiCoT Reasoning Continuous Latent Reasoning
Center for Data Science, New York University
Valid $\ne$ Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought
Jul 13, 2026Daeyeop Lee, Hwanjo YuLLM Inference Efficiency Overthinking in Language Models
1KT Corporation · 2Pohang University of Science and Technology
When Compression Helps and When It Hurts: Condition-Aware Analysis of Chain-of-Thought Distillation
Jun 19, 2026Siyang Lyu, Xinghao Chen, Zhijing Sun +3Model Compression CoT Distillation
Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo · Viterbi School of Engineering, University of Southern California · The Hong Kong Polytechnic University +2
Masked Self-Distillation: Internalizing the Chain-of-Thought in Language Models
Jun 18, 2026Durgesh Kalwar, Vardhan Palod, Jaya Adithya Pavuluri +1CoT Distillation Efficient Language Model Reasoning
SCAI, Arizona State University
SuperThoughts: Reasoning Tokens in Superposition
Jun 11, 2026Zheyang Xiong, Shivam Garg, Max Yu +4Multi-Token Prediction LLM Inference Acceleration
mMicrosoft Research · iIndependent · pPrinceton University +2
ReSum: Synergizing LLM Reasoning and Summarization with Reinforcement Learning
Jun 11, 2026Xucong Wang, Ziyu Ma, Yong Wang +5RL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
University of Science and Technology of China · 2AMAP, Alibaba Group
Compress-Distill: Reasoning Trace Compression for Efficient Knowledge Distillation
Jun 4, 2026Maxime Griot, Paul Steven Scotti, Tanishq Mathew AbrahamCoT Distillation Teacher-Student Learning
Université catholique de Louvain · Sophont Inc.
HybridThinker: Efficient Chain-of-Thought Reasoning via Compressed Memory and Transient Thought Steps
Jun 2, 2026Xin Liu, Runsong Zhao, Xinyu Liu +8Memory-Augmented Language Models Efficient Language Model Reasoning
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China · China Unicom Cloud-Link, China · NiuTrans Research, Shenyang, China
ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning
Jun 2, 2026Ziyan Liu, Xueda Shen, Yuzhe Gu +7Efficient Language Model Reasoning Preference Optimization
Shanghai Artificial Intelligence Laboratory · University of Science and Technology of China · MMLab, The Chinese University of Hong Kong
HMPO: Hybrid Median-length Policy Optimization for Chain-of-Thought Compression
Jun 1, 2026Minghui Zheng, Hongxu Chen, Huimin Ren +8LLM Compression RL for Language Model Reasoning
Li Auto Inc.
SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning
May 29, 2026Jian Yao, Xiongcai Luo, Ran Cheng +1RL for Language Model Reasoning Efficient Language Model Inference
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University · Bytedance · The Hong Kong Polytechnic University-Daya Bay Technology and Innovation Research Institute, Huizhou,Guangdong Province, China.
Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training
May 27, 2026Kohsei Matsutani, Gouki Minegishi, Takeshi Kojima +2Supervised Fine-Tuning LLM Post-Training
The University of Tokyo
Selective Latent Thinking: Adaptive Compression of LLM Reasoning Chains
May 25, 2026Hui Xie, Jie Liu, Ziyue Qiao +1Efficient Language Model Reasoning LLM Inference Acceleration
Eindhoven University of Technology, Netherlands · School of Computing and Information Technology, Great Bay University, China
Uncovering the Representation Geometry of Minimal Cores in Overcomplete Reasoning Traces
May 14, 2026Sanjoy Chowdhury, Dinesh ManochaNeural Representation Geometry LLM Interpretability
University of Maryland, College Park, USA
RuPLaR : Efficient Latent Compression of LLM Reasoning Chains with Rule-Based Priors From Multi-Step to One-Step
May 10, 2026Xiaocheng Luo, Kang Wang, Zaifu Zhan +2LLM Compression CoT Compression
School of Computer Science and Technology, Soochow University, Suzhou, 215006, Jiangsu, PR China · Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, 55455, MN, USA
Reasoning Compression with Mixed-Policy Distillation
May 9, 2026Han Yang, Mingyan Wu, Bailan He +4Language Model Distillation Efficient Language Model Inference
Technical University of Munich · GESIS – Leibniz Institute for the Social Sciences · Northeastern University +5
ExpThink: Experience-Guided Reinforcement Learning for Adaptive Chain-of-Thought Compression
May 8, 2026Tingcheng Bian, Yuzhe Zhang, Jing Jin +5Reward Shaping RL for Language Model Reasoning
1Baidu Inc. · 2Shenzhen University · 3Peking University +2
Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training
May 8, 2026Chen Wang, Hexuan Deng, Yining Zhang +5Overthinking in Language Models Reinforcement Learning with Verifiable Rewards
College of Software, Nankai University · 2Zhongguancun Academy · 3Harbin Institute of Technology +4
Structural Rationale Distillation via Reasoning Space Compression
May 8, 2026Jialin Yang, Jiankun Wang, Jiajun Wu +3CoT Distillation Structured Reasoning
University of Calgary · Calgary, Canada · University of Michigan +1
OPSD Compresses What RLVR Teaches: A Post-RL Compaction Stage for Reasoning Models
May 7, 2026Jaehoon Kim, Dongha LeeReinforcement Learning with Verifiable Rewards On-Policy Distillation
Yonsei University
Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens
Apr 29, 2026Zhenyu Zhao, Sander Land, Daniel M. Bikel +1LLM Compression LLM Interpretability
Writer, Inc.
CRISP: Compressing Redundancy in Chain-of-Thought via Intrinsic Saliency Pruning
Apr 19, 2026Yangsong Lan, Hongliang Dai, Piji LiLLM Pruning LLM Compression
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China · The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.
CRISP: Compressed Reasoning via Iterative Self-Policy Distillation
Mar 5, 2026Hejian Sang, Yuanda Xu, Zhengze Zhou +3CoT Distillation Efficient Language Model Reasoning

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Quantifying the Stability of Multi-Step Reasoning via Error Amplification

Dongyue Li, Ziniu Zhang, Minxuan Duan, Hongyang R. Zhang

Northeastern University Boston, MA

Efficient Language Model Reasoning CoT Compression

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We consider the stability of multi-step reasoning processes, which have extensive applications in language models, including chain-of-thought and algorithmic reasoning. While longer sequences of reasoning can improve a model's generation capability at test time, the errors due to intermediate reasoning steps can accumulate in autoregressive generation, and thus grow substantially at the end. In this paper, we ask: What are the key factors determining the stability of multi-step reasoning? First, we show an inference error bound governed by the product of spectral norms of the Jacobians taken through the input space across generation steps. This product can be viewed as an error amplification factor, which could scale exponentially with the number of reasoning steps, serving as a quantitative measure of reasoning stability. Second, we analyze this measure in transformer models trained to predict simple tasks like linear and quadratic functions. We theoretically prove that the transformer model converges to a solution where the stability measure decays, thus yielding nearly zero inference loss over (arbitrarily) long steps. Finally, the stability analysis leads to several algorithmic implications for controlling the stability, through (i) chain-of-thought length compression that reduces the sensitivity of each step, and (ii) quantization-aware training that regularizes the input Jacobian norms. We validate the proposed algorithms by fine-tuning language models on graph-algorithmic reasoning tasks and symbolic state-tracking tasks. Across seven evaluations, our algorithms improve over baseline comparisons by 3.5% on average, and by 8.2% for longer-length inputs. Ablation analysis validates that the stability measure is drastically reduced by 3-8

\times

, confirming the regularization effect on the spectral norms of the (input space) Jacobians.

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