{"total":101,"rows":[{"id":"2506.04178","title":"OpenThoughts: Data Recipes for Reasoning Models","created":"2025-06-04","cited_by_count":304,"area":"lr"},{"id":"2508.05004","title":"R-Zero: Self-Evolving Reasoning LLM from Zero Data","created":"2025-08-07","cited_by_count":228,"area":"lr"},{"id":"2507.01352","title":"Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy","created":"2025-07-02","cited_by_count":214,"area":"lr"},{"id":"2506.23918","title":"Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers","created":"2025-06-30","cited_by_count":199,"area":"lr"},{"id":"2509.08827","title":"A Survey of Reinforcement Learning for Large Reasoning Models","created":"2025-09-10","cited_by_count":180,"area":"lr"},{"id":"2510.25741","title":"Scaling Latent Reasoning via Looped Language Models","created":"2025-10-29","cited_by_count":165,"area":"lr"},{"id":"2506.20512","title":"OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling","created":"2025-06-25","cited_by_count":113,"area":"lr"},{"id":"2508.14444","title":"NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model","created":"2025-08-20","cited_by_count":108,"area":"lr"},{"id":"2506.06632","title":"Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning","created":"2025-06-07","cited_by_count":92,"area":"lr"},{"id":"2505.16400","title":"AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning","created":"2025-05-22","cited_by_count":87,"area":"lr"},{"id":"2506.13284","title":"AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy","created":"2025-06-16","cited_by_count":77,"area":"lr"},{"id":"2505.22375","title":"Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition","created":"2025-05-28","cited_by_count":62,"area":"lr"},{"id":"2507.16812","title":"MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning","created":"2025-07-22","cited_by_count":59,"area":"lr"},{"id":"2506.17238","title":"Training a Scientific Reasoning Model for Chemistry","created":"2025-06-04","cited_by_count":56,"area":"lr"},{"id":"2508.20722","title":"rStar2-Agent: Agentic Reasoning Technical Report","created":"2025-08-28","cited_by_count":54,"area":"lr"},{"id":"2505.21318","title":"Beyond Chemical QA: Evaluating LLM's Chemical Reasoning with Modular Chemical Operations","created":"2025-05-27","cited_by_count":49,"area":"lr"},{"id":"2512.13607","title":"Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models","created":"2025-12-15","cited_by_count":48,"area":"lr"},{"id":"2505.14183","title":"ThinkSwitcher: When to Think Hard, When to Think Fast","created":"2025-05-20","cited_by_count":37,"area":"lr"},{"id":"2510.01624","title":"Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead","created":"2025-10-02","cited_by_count":28,"area":"lr"},{"id":"2509.25666","title":"Nudging the Boundaries of LLM Reasoning","created":"2025-09-30","cited_by_count":26,"area":"lr"},{"id":"2507.09075","title":"OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique","created":"2025-07-11","cited_by_count":25,"area":"lr"},{"id":"2601.09088","title":"Distribution-Aligned Sequence Distillation for Superior Long-CoT Reasoning","created":"2026-01-14","cited_by_count":17,"area":"lr"},{"id":"2505.12366","title":"DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization","created":"2025-05-18","cited_by_count":16,"area":"lr"},{"id":"2505.20087","title":"Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models","created":"2025-05-26","cited_by_count":16,"area":"lr"},{"id":"2506.17667","title":"PhysUniBench: A Multi-Modal Physics Reasoning Benchmark at Undergraduate Level","created":"2025-06-21","cited_by_count":16,"area":"lr"},{"id":"2505.20101","title":"Adaptive Deep Reasoning: Triggering Deep Thinking When Needed","created":"2025-05-26","cited_by_count":14,"area":"lr"},{"id":"2506.02126","title":"Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains","created":"2025-06-02","cited_by_count":14,"area":"lr"},{"id":"2506.13059","title":"Multipole Attention for Efficient Long Context Reasoning","created":"2025-06-16","cited_by_count":14,"area":"lr"},{"id":"2510.12225","title":"HoneyBee: Data Recipes for Vision-Language Reasoners","created":"2025-10-14","cited_by_count":13,"area":"lr"},{"id":"2601.14249","title":"Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment","created":"2026-01-20","cited_by_count":13,"area":"lr"},{"id":"2507.10541","title":"REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once","created":"2025-07-14","cited_by_count":12,"area":"lr"},{"id":"2506.07712","title":"Through the Valley: Path to Effective Long CoT Training for Small Language Models","created":"2025-06-09","cited_by_count":11,"area":"lr"},{"id":"2512.22255","title":"Shape of Thought: When Distribution Matters More than Correctness in Reasoning Tasks","created":"2025-12-24","cited_by_count":11,"area":"lr"},{"id":"2602.17053","title":"RFEval: Benchmarking Reasoning Faithfulness under Counterfactual Reasoning Intervention in Large Reasoning Models","created":"2026-02-19","cited_by_count":11,"area":"lr"},{"id":"2510.04230","title":"Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought","created":"2025-10-05","cited_by_count":10,"area":"lr"},{"id":"2510.12000","title":"UALM: Unified Audio Language Model for Understanding, Generation and Reasoning","created":"2025-10-13","cited_by_count":10,"area":"lr"},{"id":"2510.02386","title":"On The Fragility of Benchmark Contamination Detection in Reasoning Models","created":"2025-09-30","cited_by_count":9,"area":"lr"},{"id":"2505.12717","title":"ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving","created":"2025-05-19","cited_by_count":8,"area":"lr"},{"id":"2510.08189","title":"R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?","created":"2025-10-09","cited_by_count":8,"area":"lr"},{"id":"2512.24063","title":"How and Why LLMs Generalize: A Fine-Grained Analysis of LLM Reasoning from Cognitive Behaviors to Low-Level Patterns","created":"2025-12-30","cited_by_count":8,"area":"lr"},{"id":"2505.17315","title":"Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning","created":"2025-05-22","cited_by_count":7,"area":"lr"},{"id":"2506.00668","title":"SafeTy Reasoning Elicitation Alignment for Multi-Turn Dialogues","created":"2025-05-31","cited_by_count":7,"area":"lr"},{"id":"2601.21051","title":"Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report","created":"2026-01-28","cited_by_count":7,"area":"lr"},{"id":"2605.17672","title":"Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models","created":"2026-05-17","cited_by_count":7,"area":"lr"},{"id":"2607.14552","title":"Answer-Conditioned Chains of Thought Degrade Verifiable-Reasoning Distillation in Large Language Models","created":"2026-07-16","cited_by_count":7,"area":"lr"},{"id":"2507.16802","title":"Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning","created":"2025-07-22","cited_by_count":6,"area":"lr"},{"id":"2508.03178","title":"Light-IF: Endowing LLMs with Generalizable Reasoning via Preview and Self-Checking for Complex Instruction Following","created":"2025-08-05","cited_by_count":6,"area":"lr"},{"id":"2508.12726","title":"DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoning","created":"2025-08-18","cited_by_count":6,"area":"lr"},{"id":"2512.18196","title":"LogicReward: Incentivizing LLM Reasoning via Step-Wise Logical Supervision","created":"2025-12-20","cited_by_count":6,"area":"lr"},{"id":"2603.07197","title":"$\\textbf{Re}^{2}$: Unlocking LLM Reasoning via Reinforcement Learning with Re-solving","created":"2026-03-07","cited_by_count":6,"area":"lr"},{"id":"2604.04930","title":"Early Stopping for Large Reasoning Models via Confidence Dynamics","created":"2026-04-06","cited_by_count":6,"area":"lr"},{"id":"2508.11818","title":"Audio Flamingo Sound-CoT Technical Report: Improving Chain-of-Thought Reasoning in Sound Understanding","created":"2025-08-15","cited_by_count":5,"area":"lr"},{"id":"2510.13166","title":"CoT-Evo: Evolutionary Distillation of Chain-of-Thought for Scientific Reasoning","created":"2025-10-15","cited_by_count":5,"area":"lr"},{"id":"2512.21002","title":"Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation","created":"2025-12-24","cited_by_count":5,"area":"lr"},{"id":"2602.01365","title":"When Domains Interact: Asymmetric and Order-Sensitive Cross-Domain Effects in Reinforcement Learning for Reasoning","created":"2026-02-01","cited_by_count":5,"area":"lr"},{"id":"2603.20508","title":"Measuring Weak-to-Strong Legibility of Reasoning Models","created":"2026-03-20","cited_by_count":5,"area":"lr"},{"id":"2604.12378","title":"ReasonXL: Shifting LLM Reasoning Language Without Sacrificing Performance","created":"2026-04-14","cited_by_count":5,"area":"lr"},{"id":"2605.29192","title":"ReasonOps: Operator Segmentation for LLM Reasoning Traces","created":"2026-05-28","cited_by_count":5,"area":"lr"},{"id":"2508.15868","title":"CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning","created":"2025-08-21","cited_by_count":4,"area":"lr"},{"id":"2508.19202","title":"Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning","created":"2025-08-26","cited_by_count":4,"area":"lr"},{"id":"2601.01562","title":"Logics-STEM: Empowering LLM Reasoning via Failure-Driven Post-Training and Document Knowledge Enhancement","created":"2026-01-04","cited_by_count":4,"area":"lr"},{"id":"2602.08321","title":"Improving Data and Reward Design for Scientific Reasoning in Large Language Models","created":"2026-02-09","cited_by_count":4,"area":"lr"},{"id":"2602.09782","title":"Flexible Entropy Control in RLVR with a Gradient-Preserving Perspective","created":"2026-02-10","cited_by_count":4,"area":"lr"},{"id":"2508.03333","title":"CTTS: Collective Test-Time Scaling","created":"2025-08-05","cited_by_count":3,"area":"lr"},{"id":"2509.22193","title":"Scale or Reason? A Compute-Equivalent Analysis of Reasoning Distillation","created":"2025-09-26","cited_by_count":3,"area":"lr"},{"id":"2510.00319","title":"DecepChain: Inducing Deceptive Reasoning in Large Language Models","created":"2025-09-30","cited_by_count":3,"area":"lr"},{"id":"2511.03808","title":"Optimizing Reasoning Efficiency through Prompt Difficulty Prediction","created":"2025-11-05","cited_by_count":3,"area":"lr"},{"id":"2511.19333","title":"Learning to Reason: Training LLMs with GPT-OSS or DeepSeek R1 Reasoning Traces","created":"2025-11-24","cited_by_count":3,"area":"lr"},{"id":"2512.06201","title":"K2-V2: A 360-Open, Reasoning-Enhanced LLM","created":"2025-12-05","cited_by_count":3,"area":"lr"},{"id":"2602.03485","title":"Self-Verification Dilemma: Experience-Driven Suppression of Overused Checking in LLM Reasoning","created":"2026-02-03","cited_by_count":3,"area":"lr"},{"id":"2603.21877","title":"P^2O: Joint Policy and Prompt Optimization","created":"2026-03-23","cited_by_count":3,"area":"lr"},{"id":"2605.22939","title":"Learnability-Informed Fine-Tuning of Diffusion Language Models","created":"2026-05-21","cited_by_count":3,"area":"lr"},{"id":"2508.12265","title":"Fast, Slow, and Tool-augmented Thinking for LLMs: A Review","created":"2025-08-17","cited_by_count":2,"area":"lr"},{"id":"2510.13417","title":"Assessing LLM Reasoning Through Implicit Causal Chain Discovery in Climate Discourse","created":"2025-10-15","cited_by_count":2,"area":"lr"},{"id":"2511.02651","title":"Apriel-H1: Towards Efficient Enterprise Reasoning Models","created":"2025-11-04","cited_by_count":2,"area":"lr"},{"id":"2512.10430","title":"T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground","created":"2025-12-11","cited_by_count":2,"area":"lr"},{"id":"2603.02099","title":"Recursive Think-Answer Process for LLMs and VLMs","created":"2026-03-02","cited_by_count":2,"area":"lr"},{"id":"2604.15490","title":"Think Multilingual, Not Harder: A Data-Efficient Framework for Teaching Reasoning Models to Code-Switch","created":"2026-04-16","cited_by_count":2,"area":"lr"},{"id":"2605.10805","title":"Reasoning Is Not Free: Robust Adaptive Cost-Efficient Routing for LLM-as-a-Judge","created":"2026-05-11","cited_by_count":2,"area":"lr"},{"id":"2508.05496","title":"InfiAlign: A Scalable and Sample-Efficient Framework for Aligning LLMs to Enhance Reasoning Capabilities","created":"2025-08-07","cited_by_count":1,"area":"lr"},{"id":"2602.11937","title":"Extending Puzzle for Mixture-of-Experts Reasoning Models with Application to GPT-OSS Acceleration","created":"2026-02-12","cited_by_count":1,"area":"lr"},{"id":"2603.01353","title":"Constructing Synthetic Instruction Datasets for Improving Reasoning in Domain-Specific LLMs: A Case Study in the Japanese Financial Domain","created":"2026-03-02","cited_by_count":1,"area":"lr"},{"id":"2603.22754","title":"PRISM: A Dual View of LLM Reasoning through Semantic Flow and Latent Computation","created":"2026-03-24","cited_by_count":1,"area":"lr"},{"id":"2605.22967","title":"Learned Relay Representations for Forward-Thinking Discrete Diffusion Models","created":"2026-05-21","cited_by_count":1,"area":"lr"},{"id":"2606.03965","title":"Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning","created":"2026-06-02","cited_by_count":1,"area":"lr"},{"id":"2606.17687","title":"SuCo: Sufficiency-guided Continuous Adaptive Reasoning","created":"2026-06-16","cited_by_count":1,"area":"lr"},{"id":"2511.10850","title":"Leveraging Parameter Space Symmetries for Reasoning Skill Transfer in LLMs","created":"2025-11-13","cited_by_count":0,"area":"lr"},{"id":"2602.06650","title":"Beyond Static Alignment: Hierarchical Policy Control for LLM Safety via Risk-Aware Chain-of-Thought","created":"2026-02-06","cited_by_count":0,"area":"lr"},{"id":"2603.22352","title":"WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement","created":"2026-03-22","cited_by_count":0,"area":"lr"},{"id":"2604.05757","title":"Identifying Influential N-grams in Confidence Calibration via Regression Analysis","created":"2026-04-07","cited_by_count":0,"area":"lr"},{"id":"2604.19937","title":"Infection-Reasoner: A Compact Vision-Language Model for Wound Infection Classification with Evidence-Grounded Clinical Reasoning","created":"2026-04-21","cited_by_count":0,"area":"lr"},{"id":"2604.20917","title":"The Path Not Taken: Duality in Reasoning about Program Execution","created":"2026-04-22","cited_by_count":0,"area":"lr"},{"id":"2605.20364","title":"When Reasoning Supervision Hurts: TTCW-Based Long-Form Literary Review Generation","created":"2026-05-19","cited_by_count":0,"area":"lr"},{"id":"2607.10386","title":"Structured Thoughts For Improved Reasoning And Context Pruning","created":"2026-07-11","cited_by_count":0,"area":"lr"},{"id":"2607.20448","title":"Domyn-Small: A European 10B Reasoning Language Model","created":"2026-05-13","cited_by_count":0,"area":"lr"},{"id":"2608.22854","title":"Thinking at the Right Size: Amortized Distillation Across Post-Trained LLMs","created":"2026-08-24","cited_by_count":0,"area":"lr"},{"id":"2608.26141","title":"AdaThinking-E: One-Token Entropy Regulation for Adaptive Thinking","created":"2026-06-26","cited_by_count":0,"area":"lr"},{"id":"2609.10445","title":"Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning","created":"2026-09-09","cited_by_count":0,"area":"lr"},{"id":"2609.33780","title":"Selecting Diverse SFT Traces Improves Post-RL Generalization","created":"2026-09-27","cited_by_count":0,"area":"lr"},{"id":"2609.35699","title":"Distillation Defenses Easily Break After Reinforcement Learning","created":"2026-09-28","cited_by_count":0,"area":"lr"}],"next_cursor":"100"}