Efficient Language Model Reasoning

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This field asks how language models can solve tasks such as math, coding, science questions, and multi-hop retrieval without spending unnecessary computation. Common approaches compress or prune chain-of-thought, stop generation when answers stabilize, and adapt reasoning budgets to task difficulty; others use reinforcement learning, distillation, search, parallel sampling, or smaller-model routing. Newer work also explores latent reasoning and memory or KV-cache management to reduce the cost of generating and retaining explicit thoughts. The central tension is that extra reasoning can help on difficult problems but may waste resources, introduce errors, or even hurt accuracy, so methods are judged by both quality and cost on benchmarks such as GSM8K, MATH, AIME, and GPQA.

Latest papers

373 papers89 papers in the last four weeks, up 218% on the four weeks before. 0.5% of all new papers.

Efficiently Representing Algorithms With Chain-of-Thought Transformers
Jun 18, 2026Yanhong Li, Anej Svete, Ashish Sabharwal +1Transformer CoT Reasoning
Allen Institute for AI · ETH Zürich
DreamReasoner-8B: Block-Size Curriculum Learning for Diffusion Reasoning Models
Jun 17, 2026Zirui Wu, Lin Zheng, Jiacheng Ye +5Blockwise Diffusion Language Models Efficient Language Model Reasoning
The University of Hong Kong · Peking University
Learning to Refine Hidden States for Reliable LLM Reasoning
Jun 16, 2026Chia-Hsuan Hsu, Jui-Ming YaoRL for Language Model Reasoning Efficient Language Model Reasoning
Entropy-Gated Latent Recursion
Jun 15, 2026Soham Bhattacharjee, Dushyant Singh Chauhan, Salem Lahlou +2LLM Inference Inference-Time Scaling
Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates
Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate
Jun 15, 2026Hanyu Lin, Min Cai, Jiawei Wen +1Latent Reasoning LLM Reasoning
1Shenzhen University · University of Alberta
Latent Thought Flow: Efficient Latent Reasoning in Large Language Models
Jun 15, 2026Xiandong Zou, Jing Huang, Jianshu Li +1RL for Language Model Reasoning Efficient Language Model Reasoning
Singapore Management University · Ant Group
Free Energy Heuristics: Fast-And-Frugal Cognition as Active Inference Under Uncertain Precision
Jun 14, 2026Alex BogdanCognitive Modeling Active Inference
Evolutionairy AI Toronto, Canada
DeepLook: Uncertainty-Guided Branching with Lookahead Confidence for Efficient Reasoning
Jun 12, 2026Tingxin Yang, Zefeng Wang, Mengyue Wang +4Efficient Language Model Reasoning LLM Reasoning
LMU Munich Munich, Germany · MCML, LMU, MemAgents Lab Munich, Germany
Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models
Jun 11, 2026Daniel Scalena, Sara Candussio, Luca Bortolussi +3CoT Faithfulness LLM Interpretability
CLCG, University of Groningen · University of Milano-Bicocca · University of Trieste +1
Sample Where You Struggle: Sharpening Base Model Reasoning via Entropy-Guided Power Sampling
Jun 7, 2026Hong Guo, Nianhui Guo, Christoph Meinel +1Markov Chain Monte Carlo Inference-Time Scaling
Hasso Plattner Institut · German University of Digital Science
DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling
Jun 5, 2026Tengyao Tu, Yulin Li, Hui-Ling Zhen +6Overthinking in Language Models Efficient Language Model Reasoning
Harbin Institute of Technology, Shenzhen · Zhongguancun Academy · Huawei Noah’s Ark Lab +2
When to Think Deeply: Inhibitory Deliberation for LLM Reasoning
Jun 4, 2026Zhixuan He, Yue FengEfficient Language Model Reasoning Inference-Time Compute Allocation
University of Birmingham, United Kingdom
Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning
Jun 2, 2026Yu Xia, Zhouhang Xie, Xin Xu +4Language Model Steering CoT Reasoning
University of California San Diego1 · Intuit AI Research2
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
Geometric Latent Reasoning Induces Shorter Generations in LLMs
Jun 1, 2026Shashi Kumar, Yacouba Kaloga, Petr Motlicek +2Efficient Language Model Reasoning Continuous Latent Reasoning
Idiap Research Institute, Switzerland · EPFL, Switzerland · BUT, Czech Republic
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery
Jun 1, 2026Ekaterina Alimaskina, Darya Rudas, Denis Shveykin +3LLM Quantization Repetition in Language Generation
BRAIn Lab, Moscow, Russia
Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs
May 31, 2026Yubo Gao, Haotian Wu, Hong Chen +8LLM Inference Efficiency CoT Reasoning
The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology · Nanyang Technological University +1
ThinkSwitch: Context Distillation with LoRA and Weight Interpolation for Specific-Purpose Reasoning Tasks
May 31, 2026Dhruv Saini, Rohan PandeyEfficient Language Model Reasoning Language Model Distillation
Bellevue High School · DigitalOcean
Quantized Reasoning Models Think They Need to Think Longer, but They Do Not
May 29, 2026Sanae Lotfi, Polina Kirichenko, Steven Li +1Logit Adjustment Overthinking in Language Models
1FAIR at Meta · 2Meta AI
Unlocking the Working Memory of Large Language Models for Latent Reasoning
May 28, 2026Lukas Aichberger, Sepp HochreiterImplicit Reasoning in Language Models Efficient Language Model Reasoning
ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria · NXAI GmbH, Linz, Austria
Reasoning with Sampling: Cutting at Decision Points
May 28, 2026Felix Zhou, Anay Mehrotra, Quanquan C. LiuMarkov Chain Monte Carlo Efficient Language Model Reasoning
Yale University · Stanford University
ESPO: Early-Stopping Proximal Policy Optimization
May 28, 2026Zihang Li, Rui Zhou, Yingcheng Shi +8LLM Reasoning Reinforcement Learning
1Tongyi Lab , Alibaba Group · 2Peking University
Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies
May 28, 2026Yuxuan Ye, Raul Santos-Rodriguez, Edwin SimpsonLLM Grounding Claim Verification
Intelligent Systems Laboratory University of Bristol United Kingdom
CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning
May 27, 2026Linas Nasvytis, Simon Jerome Han, Ben Prystawski +3LLM Self-Refinement Efficient Language Model Reasoning
Stanford University
CIRF: Tokenizing Chain-of-Thoughts into Reusable Functional Units for Efficient Latent Reasoning in Large Language Models
May 27, 2026Yukyung Lee, Yumeng Shen, Jinhyeong Park +2CoT Reasoning Efficient Language Model Reasoning
Boston University · Hankuk University of Foreign Studies
Self-Consistency via Marginal Sharpening
May 27, 2026Aleksei Arzhantsev, Otmane Sakhi, Nicolas ChopinEfficient Language Model Reasoning Reasoning Consistency in Language Models
Criteo AI Lab, CREST IP Paris · Criteo AI Lab · ENSAE, Institut Polytechnique de Paris
Uncertainty-Aware Budget Allocation for Adaptive Test-Time Reasoning
May 26, 2026Manh Nguyen, Sunil Gupta, Hung LeEfficient Language Model Reasoning Adaptive Sampling
Applied Artificial Intelligence Initiative Deakin University, Australia
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
Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling
May 24, 2026Dao Tran, Duc Anh Le, Ngoc Luu +3Inference-Time Scaling Efficient Language Model Reasoning
Qualcomm AI Research†

Jun 18, 2026J/K move · Enter open · S save

Efficiently Representing Algorithms With Chain-of-Thought Transformers

Yanhong Li, Anej Svete, Ashish Sabharwal, William Merrill

Allen Institute for AI · ETH Zürich

Transformer CoT Reasoning Neural Algorithmic Reasoning Efficient Language Model Reasoning Algorithmic Reasoning

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The increasing popularity of reasoning models -- language models that output a series of reasoning or thought tokens before producing an answer -- is justified, in part, by theoretical results showing that chain-of-thought (CoT) transformers can simulate Turing machines, and thus perform arbitrary computation. However, the Turing machine, while suitable for complexity-theoretic analysis, is not convenient, intuitive, or efficient for discussing algorithms. Algorithms are typically designed and analyzed at a higher level of abstraction, captured by the Word RAM model with random-access memory and unit-cost operations on

\bigO(\log n)

-bit words. As a result, Word RAM algorithms can be substantially more efficient than their Turing machine counterparts, raising the question: Can CoT transformers efficiently simulate Word RAM algorithms? For instance, can they sort

n

items in

\bigO(n \log n)

steps or run Dijkstra's algorithm in

\bigO(E + V \log V)

steps? We answer affirmatively, up to poly-logarithmic overhead. We first establish this for finite-precision transformers with poly-logarithmic width and rightmost unique hard attention, then strengthen the result to two more practical settings with finite width and log-precision: continuous CoT, where reasoning takes the form of vectors rather than tokens, and a hybrid architecture in which transformer layers sit atop a recurrent (linear RNN) layer. In all three cases, we find that CoT can efficiently simulate any Word RAM algorithm with only a poly-logarithmic overhead in

n

. This overhead reduces to log-square when the Word RAM has a ``flat'' instruction set, and only logarithmic for multiplication-free flat instructions -- in stark contrast to known CoT simulations of Turing machines, which require quadratic overhead over Word RAM.