Efficient Language Model Reasoning

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SCOPE-RL: Optimizing Reasoning Paths Before and After Success
Jul 13, 2026Xiaojian Liu, Han Xu, Jianqiang Xia +6Reward Shaping RL for Language Model Reasoning
Baidu Inc. · Shandong University
OS-Pruner: Pruning Chains-of-Thought of Reasoning Models via Optimal Stopping
Jul 13, 2026Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias +2Overthinking in Language Models Sequential Decision Making
MIT · Columbia Business School
Structured Thoughts For Improved Reasoning And Context Pruning
Jul 11, 2026Zain Sarwar, Supriyo Chakraborty, Berkcan Kapusuzoglu +5CoT Reasoning Efficient Language Model Reasoning
University of Chicago · Capital One
Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning
Jul 9, 2026Yiyang Fang, Pei Fu, Jinjie Li +7Efficient Multimodal Inference RL for Language Model Reasoning
Wuhan University · The Hong Kong University of Science and Technology · Xiaomi Inc +2
Forethought: Verifiable Reasoning from Neurosymbolic Primitive Programming
Jul 5, 2026Vishvesh Bhat, Jay Vaghasiya, Emmanuel Anaya GonzalezSymbolic Reasoning Efficient Language Model Reasoning
CoreThink AI · UC San Diego
Diffusion-GR2: Diffusion Generative Reasoning Re-ranker
Jul 1, 2026Zhuoxuan Zhang, Kangqi Ni, Yuhang Chen +12Blockwise Diffusion Language Models Efficient Language Model Reasoning
Yang · 2UNC Chapel Hill · 1Meta AI
Message Passing Enables Efficient Reasoning
Jul 1, 2026Xuecheng Liu, Daman Arora, Gokul Swamy +1Inference-Time Scaling Efficient Language Model Reasoning
Carnegie Mellon University
Efficient Multilingual Reasoning Transfer via Progressive Code-Switching
Jul 1, 2026Zhijun Wang, Junxiao Liu, Hao Zhou +3Code-Switching Cross-Lingual Reasoning in Language Models
National Key Laboratory for Novel Software Technology, Nanjing University, China · Tongyi Lab, Alibaba Group
Know When to Stop: Segment-Level Credit Assignment for Reducing Overthinking
Jul 1, 2026Chia-Hsuan Lee, Sihui Dai, Mingyang Zhou +4LLM Self-Correction Overthinking in Language Models
Capital One
CoLT: Teaching Multi-Modal Models to Think with Chain of Latent Thoughts
Jun 30, 2026Lianyu Hu, Shengqian Qin, Zeqin Liao +4Efficient Multimodal Inference Efficient Language Model Reasoning
Nanyang Technological University, Singapore · Shanghai Jiao Tong University, China · NKIARI, Shenzhen Futian, China +2
Fork-Think with Confidence
Jun 30, 2026Zena Al-Khalili, Rafi Hakim, Dietrich Klakow +1LLM Inference Efficiency Efficient Language Model Reasoning
Saarland Informatics Campus, Saarland University, Germany
When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models
Jun 29, 2026Zhe Dong, Fang Qin, Manish ShahEarly Stopping Cost-Aware Inference
University of Maine at Presque Isle · Stanford University · Independent Researcher
Why Struggle with Continuous Latents? Interpretable Discrete Latent Reasoning via Rendered Compression
Jun 29, 2026Shuochen Chang, Qingyang Liu, Shaobo Wang +8LLM Interpretability Efficient Language Model Reasoning
Shanghai Jiao Tong University · Tongji University
When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
Jun 28, 2026Zhengqi Pei, Qingming Huang, Shuhui WangMulti-Agent LLM Systems Efficient Language Model Reasoning
State Key Lab of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. · School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China.
Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models
Jun 24, 2026Xinyu Lian, Walid Krichene, Beichen Huang +4LLM Inference Efficiency LLM Quantization
University of Illinois Urbana-Champaign · Microsoft · Anyscale +1
Efficient and Trainable Language Model Test-Time Scaling via Local Branch Routing
Jun 24, 2026Yutong Yin, Mingyu Jin, Jin Pan +12RL for Language Model Reasoning Test-Time Scaling
1Northwestern University · 2Rutgers University · University of Wisconsin–Madison +7
SPIRAL: Learning to Search and Aggregate
Jun 22, 2026Jubayer Ibn Hamid, Ifdita Hasan Orney, Michael Y. Li +5Reinforcement Learning RL for Language Model Reasoning
Stanford University
Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently
Jun 22, 2026Stanley Wei, Juno KimRL for Language Model Reasoning Efficient Language Model Reasoning
1Princeton University · University of California, Berkeley.
Beyond Penalizing Mistakes: Stabilizing Efficiency Training in Large Reasoning Models via Adaptive Correct-Only Rewards
Jun 21, 2026Jungseob Lee, Seungyoon Lee, Seongtae Hong +3Reward Shaping RL for Language Model Reasoning
Korea University · Soongsil University
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
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 +1Language Model Decoding Efficient Language Model 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
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

Jul 13, 2026, cs.LGJ/K move · Enter open · S save

SCOPE-RL: Optimizing Reasoning Paths Before and After Success

Xiaojian Liu, Han Xu, Jianqiang Xia, Zhixuan Li+5

Baidu Inc. · Shandong University

Reward Shaping RL for Language Model Reasoning Efficient Language Model Reasoning Reinforcement Learning with Verifiable Rewards Reward Design for RL

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Reinforcement learning with verifiable rewards (RLVR) optimizes LLMs using sparse verifiable final-answer rewards. This sparse anchor reliably verifies whether a trajectory succeeds but provides no direct feedback on the reasoning path that produced it. Before success, prerequisite progress on hard problems receives no reward signal; after success, outcome rewards cannot distinguish well-organized correct trajectories from redundant or locally flawed ones. We introduce SCOPE-RL (Scaffolded Chain Optimization with Process Efficiency), a two-stage framework that densifies this anchor while retaining the GRPO update: Adaptive Scaffolded RL adds prefix-decomposed verifiable rewards on answer-hidden sub-question chains before success, and Quality-Aware Process RL applies correctness-gated process-shape rewards to refine correct trajectories after success. An expert-validated Step-Quality Evaluation Protocol evaluates useful-step density, error localization, and token efficiency beyond final-answer accuracy. On Qwen3-8B-Instruct trained on DAPO-Math and Big-Math, SCOPE-RL improves average accuracy by up to 11.2 pp and reduces reasoning tokens by up to 27.1% over outcome-only GRPO; the gains hold under GSPO and on Qwen3-0.6B-Instruct, indicating that reward-signal densification is complementary to policy-update-level RLVR advances. Code and data are available at https://github.com/tokencraft-lab/SCOPE-RL.

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