{"total":46,"rows":[{"id":"2508.18265","title":"InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency","created":"2025-08-25","cited_by_count":1510,"area":"lr"},{"id":"2502.01456","title":"Process Reinforcement through Implicit Rewards","created":"2025-02-03","cited_by_count":504,"area":"lr"},{"id":"2503.09567","title":"Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models","created":"2025-03-12","cited_by_count":384,"area":"lr"},{"id":"2504.20571","title":"Reinforcement Learning for Reasoning in Large Language Models with One Training Example","created":"2025-04-29","cited_by_count":295,"area":"lr"},{"id":"2503.15558","title":"Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning","created":"2025-03-18","cited_by_count":164,"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":"2503.21380","title":"Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models","created":"2025-03-27","cited_by_count":70,"area":"lr"},{"id":"2501.17703","title":"Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate","created":"2025-01-29","cited_by_count":63,"area":"lr"},{"id":"2506.09026","title":"e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs","created":"2025-06-10","cited_by_count":59,"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":"2509.15965","title":"RLinf: Flexible and Efficient Large-scale Reinforcement Learning via Macro-to-Micro Flow Transformation","created":"2025-09-19","cited_by_count":57,"area":"lr"},{"id":"2509.06870","title":"The Majority is not always right: RL training for solution aggregation","created":"2025-09-08","cited_by_count":47,"area":"lr"},{"id":"2502.17407","title":"Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning","created":"2025-02-24","cited_by_count":37,"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":"2510.07242","title":"Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Dense","created":"2025-10-08","cited_by_count":20,"area":"lr"},{"id":"2508.11016","title":"CURE: Critical-Token-Guided Re-Concatenation for Entropy-Collapse Prevention","created":"2025-08-14","cited_by_count":18,"area":"lr"},{"id":"2505.14140","title":"RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning","created":"2025-05-20","cited_by_count":16,"area":"lr"},{"id":"2507.10616","title":"Scalpel vs. Hammer: GRPO Amplifies Existing Capabilities, SFT Replaces Them","created":"2025-07-13","cited_by_count":15,"area":"lr"},{"id":"2504.06214","title":"From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models","created":"2025-04-08","cited_by_count":14,"area":"lr"},{"id":"2505.02686","title":"Sailing by the Stars: A Survey on Reward Models and Learning Strategies for Learning from Rewards","created":"2025-05-05","cited_by_count":14,"area":"lr"},{"id":"2507.17797","title":"GenSelect: A Generative Approach to Best-of-N","created":"2025-07-23","cited_by_count":14,"area":"lr"},{"id":"2503.13288","title":"$\\phi$-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation","created":"2025-03-17","cited_by_count":13,"area":"lr"},{"id":"2502.06217","title":"Examining False Positives under Inference Scaling for Mathematical Reasoning","created":"2025-02-10","cited_by_count":11,"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":"2509.24193","title":"AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play","created":"2025-09-29","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":"2507.03133","title":"ReliableMath: Benchmark of Reliable Mathematical Reasoning on Large Language Models","created":"2025-07-03","cited_by_count":8,"area":"lr"},{"id":"2510.01925","title":"Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey","created":"2025-10-02","cited_by_count":8,"area":"lr"},{"id":"2511.21581","title":"Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning","created":"2025-11-26","cited_by_count":7,"area":"lr"},{"id":"2602.09305","title":"Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation","created":"2026-02-10","cited_by_count":7,"area":"lr"},{"id":"2512.19081","title":"Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning","created":"2025-12-22","cited_by_count":6,"area":"lr"},{"id":"2506.06470","title":"SIGMA: Refining Large Language Model Reasoning via Sibling-Guided Monte Carlo Augmentation","created":"2025-06-06","cited_by_count":4,"area":"lr"},{"id":"2510.13170","title":"Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanism","created":"2025-10-15","cited_by_count":4,"area":"lr"},{"id":"2506.03295","title":"Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem","created":"2025-06-03","cited_by_count":3,"area":"lr"},{"id":"2603.18886","title":"Reasoning over mathematical objects: on-policy reward modeling and test time aggregation","created":"2026-03-19","cited_by_count":3,"area":"lr"},{"id":"2507.19748","title":"JT-Math: A Multi-Stage Framework for Advanced Mathematical Reasoning in Large Language Models","created":"2025-07-26","cited_by_count":2,"area":"lr"},{"id":"2602.02143","title":"Learning Generative Selection for Best-of-N","created":"2026-02-02","cited_by_count":2,"area":"lr"},{"id":"2607.10139","title":"LLMs as a Jury: Cross-Model Consensus Can Outperform Process Reward Models for LLM Reasoning","created":"2026-07-11","cited_by_count":2,"area":"lr"},{"id":"2509.23152","title":"Critique to Verify: Accurate and Honest Test-Time Scaling with RL-Trained Verifiers","created":"2025-09-27","cited_by_count":1,"area":"lr"},{"id":"2602.06291","title":"Judging What We Cannot Solve: A Consequence-Based Approach for Oracle-Free Evaluation of Research-Level Math","created":"2026-02-06","cited_by_count":1,"area":"lr"},{"id":"2602.17993","title":"Turbo Connection: Reasoning as Information Flow from Higher to Lower Layers","created":"2026-02-20","cited_by_count":1,"area":"lr"},{"id":"2603.01025","title":"One-Token Verification for Reasoning Correctness Estimation","created":"2026-03-01","cited_by_count":1,"area":"lr"},{"id":"2604.18936","title":"Fine-Tuning Small Reasoning Models for Quantum Field Theory","created":"2026-04-21","cited_by_count":1,"area":"lr"},{"id":"2508.08665","title":"Aryabhata: An exam-focused language model for JEE Math","created":"2025-08-12","cited_by_count":0,"area":"lr"},{"id":"2609.39220","title":"Learning Process Rewards via Reasoning State Propagation","created":"2026-09-30","cited_by_count":0,"area":"lr"}],"next_cursor":null}