{"total":54,"rows":[{"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":"2503.24235","title":"A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?","created":"2025-03-31","cited_by_count":223,"area":"lr"},{"id":"2504.11343","title":"A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce","created":"2025-04-15","cited_by_count":145,"area":"lr"},{"id":"2502.21321","title":"LLM Post-Training: A Deep Dive into Reasoning Large Language Models","created":"2025-02-28","cited_by_count":135,"area":"lr"},{"id":"2503.15478","title":"SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks","created":"2025-03-19","cited_by_count":118,"area":"lr"},{"id":"2602.02482","title":"Expanding the Capabilities of Reinforcement Learning via Text Feedback","created":"2026-02-02","cited_by_count":53,"area":"alignment"},{"id":"2504.04736","title":"Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use","created":"2025-04-07","cited_by_count":45,"area":"lr"},{"id":"2510.01135","title":"Prompt Curriculum Learning for Efficient LLM Post-Training","created":"2025-10-01","cited_by_count":45,"area":null},{"id":"2502.12853","title":"S$^2$R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning","created":"2025-02-18","cited_by_count":39,"area":"lr"},{"id":"2506.07636","title":"SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling","created":"2025-06-09","cited_by_count":26,"area":"agents"},{"id":"2505.20686","title":"Accelerating RL for LLM Reasoning with Optimal Advantage Regression","created":"2025-05-27","cited_by_count":24,"area":"lr"},{"id":"2512.12812","title":"Does Tone Change the Answer? Evaluating Prompt Politeness Effects on Modern LLMs: GPT, Gemini, and LLaMA","created":"2025-12-14","cited_by_count":23,"area":null},{"id":"2601.08605","title":"ExpSeek: Self-Triggered Experience Seeking for Web Agents","created":"2026-01-13","cited_by_count":23,"area":"agents"},{"id":"2507.04136","title":"A Technical Survey of Reinforcement Learning Techniques for Large Language Models","created":"2025-07-05","cited_by_count":21,"area":"lr"},{"id":"2504.21561","title":"Iterative Tool Usage Exploration for Multimodal Agents via Step-wise Preference Tuning","created":"2025-04-30","cited_by_count":19,"area":"mllm"},{"id":"2506.17670","title":"Online Multi-LLM Selection via Contextual Bandits under Unstructured Context Evolution","created":"2025-06-21","cited_by_count":19,"area":null},{"id":"2507.08806","title":"Think Clearly: Improving Reasoning via Redundant Token Pruning","created":"2025-06-17","cited_by_count":17,"area":"lr"},{"id":"2502.14333","title":"A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics","created":"2025-02-20","cited_by_count":11,"area":"lr"},{"id":"2505.15311","title":"Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning","created":"2025-05-21","cited_by_count":11,"area":"lr"},{"id":"2512.07533","title":"VulnLLM-R: Specialized Reasoning LLM with Agent Scaffold for Vulnerability Detection","created":"2025-12-08","cited_by_count":11,"area":"lr"},{"id":"2602.01058","title":"Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning","created":"2026-02-01","cited_by_count":11,"area":"lr"},{"id":"2501.18858","title":"BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning","created":"2025-01-31","cited_by_count":10,"area":"lr"},{"id":"2503.12123","title":"MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling","created":"2025-03-15","cited_by_count":10,"area":"lr"},{"id":"2505.20561","title":"Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning","created":"2025-05-26","cited_by_count":9,"area":"lr"},{"id":"2506.23127","title":"Unleashing Embodied Task Planning Ability in LLMs via Reinforcement Learning","created":"2025-06-29","cited_by_count":9,"area":null},{"id":"2508.16665","title":"Trust but Verify! A Survey on Verification Design for Test-time Scaling","created":"2025-08-20","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":"2602.09138","title":"PABU: Progress-Aware Belief Update for Efficient LLM Agents","created":"2026-02-09","cited_by_count":7,"area":"agents"},{"id":"2602.11767","title":"TSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents","created":"2026-02-12","cited_by_count":7,"area":"agents"},{"id":"2502.13723","title":"Direct Value Optimization: Improving Chain-of-Thought Reasoning in LLMs with Refined Values","created":"2025-02-19","cited_by_count":6,"area":"lr"},{"id":"2506.02553","title":"Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective","created":"2025-06-03","cited_by_count":5,"area":"alignment"},{"id":"2510.19949","title":"Surfer 2: The Next Generation of Cross-Platform Computer Use Agents","created":"2025-10-22","cited_by_count":5,"area":"agents"},{"id":"2510.08539","title":"On the optimization dynamics of RLVR: Gradient gap and step size thresholds","created":"2025-10-09","cited_by_count":4,"area":null},{"id":"2512.08889","title":"No Labels, No Problem: Training Visual Reasoners with Multimodal Verifiers","created":"2025-12-09","cited_by_count":4,"area":"lr"},{"id":"2603.12011","title":"Can RL Improve Generalization of LLM Agents? An Empirical Study","created":"2026-03-12","cited_by_count":4,"area":"agents"},{"id":"2603.13985","title":"Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models","created":"2026-03-14","cited_by_count":3,"area":null},{"id":"2509.04501","title":"Understanding Reinforcement Learning for Model Training, and future directions with GRAPE","created":"2025-09-02","cited_by_count":2,"area":"lr"},{"id":"2510.24126","title":"Reinforcement Learning for Long-Horizon Multi-Turn Search Agents","created":"2025-10-28","cited_by_count":2,"area":"lr"},{"id":"2606.07812","title":"Scaling Participation in Modular AI Systems","created":"2026-06-05","cited_by_count":2,"area":null},{"id":"2606.21943","title":"Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning","created":"2026-06-20","cited_by_count":2,"area":"lr"},{"id":"2609.05837","title":"AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories","created":"2026-09-05","cited_by_count":2,"area":"agents"},{"id":"2502.05773","title":"PIPA: Preference Alignment as Prior-Informed Statistical Estimation","created":"2025-02-09","cited_by_count":1,"area":"alignment"},{"id":"2509.19893","title":"Future Policy Approximation for Offline Reinforcement Learning in LLM Reasoning","created":"2025-09-24","cited_by_count":1,"area":"lr"},{"id":"2509.25148","title":"AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models","created":"2025-09-29","cited_by_count":1,"area":"alignment"},{"id":"2510.08567","title":"MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning","created":"2025-10-09","cited_by_count":1,"area":"lr"},{"id":"2603.05294","title":"STRUCTUREDAGENT: Planning with AND/OR Trees for Long-Horizon Web Tasks","created":"2026-03-05","cited_by_count":1,"area":"agents"},{"id":"2503.21544","title":"SWI: Speaking with Intent in Large Language Models","created":"2025-03-27","cited_by_count":0,"area":null},{"id":"2602.15913","title":"Foundation Models for Medical Imaging: Status, Challenges, and Directions","created":"2026-02-17","cited_by_count":0,"area":null},{"id":"2606.11470","title":"The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes","created":"2026-06-09","cited_by_count":0,"area":"lr"},{"id":"2606.17735","title":"Shattering the Autoregressive Curse: Dynamic Epistemic Entropy Orchestrated Erasable Reinforcement Learning for LLMs","created":"2026-06-16","cited_by_count":0,"area":"lr"},{"id":"2607.21856","title":"LeAct: Learning to Reason from Expert Actions","created":"2026-07-23","cited_by_count":0,"area":"lr"},{"id":"2607.22642","title":"CRAFT: Learn the Schema, Execute the Plan","created":"2026-06-24","cited_by_count":0,"area":"agents"},{"id":"2608.07719","title":"CODS: Iterative Bellman-Residual Data Selection for Reusable Offline Reinforcement Learning","created":"2026-08-07","cited_by_count":0,"area":"offlinerl"},{"id":"2610.02703","title":"Learning to Revise Reasoning with Segment-wise On-Policy Distillation","created":"2026-10-02","cited_by_count":0,"area":"lr"}],"next_cursor":null}