{"total":191,"rows":[{"id":"2501.15383","title":"Qwen2.5-1M Technical Report","created":"2025-01-26","cited_by_count":164,"area":null},{"id":"2503.22679","title":"Q-Insight: Understanding Image Quality via Visual Reinforcement Learning","created":"2025-03-28","cited_by_count":98,"area":null},{"id":"2502.03438","title":"BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving","created":"2025-02-05","cited_by_count":84,"area":null},{"id":"2511.10507","title":"AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following","created":"2025-11-13","cited_by_count":51,"area":null},{"id":"2511.21689","title":"ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration","created":"2025-11-26","cited_by_count":47,"area":null},{"id":"2510.01135","title":"Prompt Curriculum Learning for Efficient LLM Post-Training","created":"2025-10-01","cited_by_count":45,"area":null},{"id":"2411.03766","title":"Number Cookbook: Number Understanding of Language Models and How to Improve It","created":"2024-11-06","cited_by_count":42,"area":null},{"id":"2509.24372","title":"Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning","created":"2025-09-29","cited_by_count":34,"area":null},{"id":"2601.14525","title":"Towards Execution-Grounded Automated AI Research","created":"2026-01-20","cited_by_count":28,"area":null},{"id":"2412.03205","title":"U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs","created":"2024-12-04","cited_by_count":24,"area":null},{"id":"2510.02453","title":"How to Train Your Advisor: Steering Black-Box LLMs with Advisor Models","created":"2025-10-02","cited_by_count":24,"area":null},{"id":"2505.24480","title":"Towards Effective Code-Integrated Reasoning","created":"2025-05-30","cited_by_count":23,"area":null},{"id":"2509.25849","title":"Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget Allocation","created":"2025-09-30","cited_by_count":22,"area":null},{"id":"2510.23081","title":"A Survey on LLM Mid-Training","created":"2025-10-27","cited_by_count":21,"area":null},{"id":"2509.04784","title":"Post-training Large Language Models for Diverse High-Quality Responses","created":"2025-09-05","cited_by_count":19,"area":null},{"id":"2502.11027","title":"On the Effect of Sampling Diversity in Scaling LLM Inference","created":"2025-02-16","cited_by_count":18,"area":null},{"id":"2505.12392","title":"SLOT: Sample-specific Language Model Optimization at Test-time","created":"2025-05-18","cited_by_count":18,"area":null},{"id":"2506.07047","title":"Mathesis: Towards Formal Theorem Proving from Natural Languages","created":"2025-06-08","cited_by_count":18,"area":null},{"id":"2505.00662","title":"DeepCritic: Deliberate Critique with Large Language Models","created":"2025-05-01","cited_by_count":17,"area":null},{"id":"2505.24138","title":"AMSbench: A Comprehensive Benchmark for Evaluating MLLM Capabilities in AMS Circuits","created":"2025-05-30","cited_by_count":15,"area":null},{"id":"2503.22456","title":"Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning","created":"2025-03-28","cited_by_count":13,"area":null},{"id":"2504.07866","title":"Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs","created":"2025-04-10","cited_by_count":13,"area":null},{"id":"2506.00777","title":"Improving Automatic Evaluation of Large Language Models (LLMs) in Biomedical Relation Extraction via LLMs-as-the-Judge","created":"2025-06-01","cited_by_count":13,"area":null},{"id":"2604.07666","title":"An Imperfect Verifier is Good Enough: Learning with Noisy Rewards","created":"2026-04-09","cited_by_count":13,"area":null},{"id":"2502.12171","title":"GoRA: Gradient-driven Adaptive Low Rank Adaptation","created":"2025-02-13","cited_by_count":12,"area":null},{"id":"2509.17946","title":"HICode: Hierarchical Inductive Coding with LLMs","created":"2025-09-22","cited_by_count":12,"area":null},{"id":"2412.15184","title":"Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning","created":"2024-12-19","cited_by_count":11,"area":null},{"id":"2504.07440","title":"Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric","created":"2025-04-10","cited_by_count":11,"area":null},{"id":"2509.16591","title":"Heterogeneous Adaptive Policy Optimization: Tailoring Optimization to Every Token's Nature","created":"2025-09-20","cited_by_count":11,"area":null},{"id":"2510.17776","title":"Mapping Post-Training Forgetting in Language Models at Scale","created":"2025-10-20","cited_by_count":11,"area":null},{"id":"2508.09662","title":"EffiEval: Efficient and Generalizable Model Evaluation via Capability Coverage Maximization","created":"2025-08-13","cited_by_count":9,"area":null},{"id":"2602.01745","title":"Probability-Entropy Calibration: An Elastic Indicator for Adaptive Fine-tuning","created":"2026-02-02","cited_by_count":9,"area":null},{"id":"2602.05879","title":"EuroLLM-22B: Technical Report","created":"2026-02-05","cited_by_count":9,"area":null},{"id":"2409.18893","title":"HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models","created":"2024-09-27","cited_by_count":8,"area":null},{"id":"2505.18502","title":"Knowledge Fusion of Large Language Models Via Modular SkillPacks","created":"2025-05-24","cited_by_count":8,"area":null},{"id":"2512.03442","title":"PretrainZero: Reinforcement Active Pretraining","created":"2025-12-03","cited_by_count":8,"area":null},{"id":"2605.20270","title":"Conformal Selective Acting: Anytime-Valid Risk Control for RLVR-Trained LLMs","created":"2026-05-18","cited_by_count":8,"area":null},{"id":"2412.17743","title":"YuLan-Mini: An Open Data-efficient Language Model","created":"2024-12-23","cited_by_count":7,"area":null},{"id":"2509.01395","title":"LLMs cannot spot math errors, even when allowed to peek into the solution","created":"2025-09-01","cited_by_count":7,"area":null},{"id":"2602.03584","title":"$V_0$: A Generalist Value Model for Any Policy at State Zero","created":"2026-02-03","cited_by_count":7,"area":null},{"id":"2411.06824","title":"Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs","created":"2024-11-11","cited_by_count":6,"area":null},{"id":"2411.18872","title":"A Lean Dataset for International Math Olympiad: Small Steps towards Writing Math Proofs for Hard Problems","created":"2024-11-28","cited_by_count":6,"area":null},{"id":"2507.17147","title":"CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards","created":"2025-07-23","cited_by_count":6,"area":null},{"id":"2508.21188","title":"Mirage or Method? How Model-Task Alignment Induces Divergent RL Conclusions","created":"2025-08-28","cited_by_count":6,"area":null},{"id":"2510.09259","title":"Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models","created":"2025-10-10","cited_by_count":6,"area":null},{"id":"2601.06851","title":"A Brain-like Synergistic Core in LLMs Drives Behaviour and Learning","created":"2026-01-11","cited_by_count":6,"area":null},{"id":"2602.12036","title":"Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models","created":"2026-02-12","cited_by_count":6,"area":null},{"id":"2602.13273","title":"MergePipe: A Budget-Aware Parameter Management System for Scalable LLM Merging","created":"2026-02-05","cited_by_count":6,"area":null},{"id":"2604.19749","title":"The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?","created":"2026-03-03","cited_by_count":6,"area":null},{"id":"2606.14502","title":"From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI","created":"2026-06-12","cited_by_count":6,"area":null},{"id":"2411.16454","title":"Learning by Analogy: Enhancing Few-Shot Prompting for Math Word Problem Solving with Computational Graph-Based Retrieval","created":"2024-11-25","cited_by_count":5,"area":null},{"id":"2502.16040","title":"Inference Computation Scaling for Feature Augmentation in Recommendation Systems","created":"2025-02-22","cited_by_count":5,"area":null},{"id":"2503.21383","title":"Controlling Large Language Model with Latent Actions","created":"2025-03-27","cited_by_count":5,"area":null},{"id":"2504.07527","title":"Supervised Optimism Correction: Be Confident When LLMs Are Sure","created":"2025-04-10","cited_by_count":5,"area":null},{"id":"2601.19001","title":"FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning","created":"2026-01-26","cited_by_count":5,"area":null},{"id":"2602.06843","title":"The Representational Geometry of Number","created":"2026-02-06","cited_by_count":5,"area":null},{"id":"2605.26606","title":"Spend Your Rollouts Where It Counts: Rollout Allocation for Group-Based RL Post-Training","created":"2026-05-26","cited_by_count":5,"area":null},{"id":"2412.13464","title":"GenX: Mastering Code and Test Generation with Execution Feedback","created":"2024-12-18","cited_by_count":4,"area":null},{"id":"2503.14917","title":"MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models","created":"2025-03-19","cited_by_count":4,"area":null},{"id":"2503.19123","title":"Overcoming Vocabulary Mismatch: Vocabulary-agnostic Teacher Guided Language Modeling","created":"2025-03-24","cited_by_count":4,"area":null},{"id":"2505.20209","title":"Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection","created":"2025-05-26","cited_by_count":4,"area":null},{"id":"2506.07824","title":"Addition in Four Movements: Mapping Layer-wise Information Trajectories in LLMs","created":"2025-06-09","cited_by_count":4,"area":null},{"id":"2509.01328","title":"Can Large Language Models Master Complex Card Games?","created":"2025-09-01","cited_by_count":4,"area":null},{"id":"2510.06627","title":"POME: Post Optimization Model Edit via Muon-style Projection","created":"2025-10-08","cited_by_count":4,"area":null},{"id":"2510.08549","title":"Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints","created":"2025-10-09","cited_by_count":4,"area":null},{"id":"2510.09359","title":"Understanding the Effects of Domain Finetuning on LLMs","created":"2025-10-10","cited_by_count":4,"area":null},{"id":"2510.24505","title":"CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?","created":"2025-10-28","cited_by_count":4,"area":null},{"id":"2603.09892","title":"MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning","created":"2026-03-10","cited_by_count":4,"area":null},{"id":"2604.01597","title":"Learning from the Right Rollouts: Data Attribution for PPO-based LLM Post-Training","created":"2026-04-02","cited_by_count":4,"area":null},{"id":"2605.14967","title":"InfoSFT: Learn More and Forget Less with Information-Aware Token Weighting","created":"2026-05-14","cited_by_count":4,"area":null},{"id":"2503.22547","title":"Bridging the Dimensional Chasm: Uncover Layer-wise Dimensional Reduction in Transformers through Token Correlation","created":"2025-03-28","cited_by_count":3,"area":null},{"id":"2507.18263","title":"Locate-and-Focus: Enhancing Terminology Translation in Speech Language Models","created":"2025-07-24","cited_by_count":3,"area":null},{"id":"2508.07697","title":"Semantic-Enhanced Time-Series Forecasting via Large Language Models","created":"2025-08-11","cited_by_count":3,"area":null},{"id":"2508.08882","title":"Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning","created":"2025-08-12","cited_by_count":3,"area":null},{"id":"2510.12224","title":"MedKGEval: A Knowledge Graph-Based Multi-Turn Evaluation Framework for Open-Ended Patient Interactions with Clinical LLMs","created":"2025-10-14","cited_by_count":3,"area":null},{"id":"2510.12721","title":"CARVQ: Corrective Adaptor with Group Residual Vector Quantization for LLM Embedding Compression","created":"2025-10-14","cited_by_count":3,"area":null},{"id":"2510.15346","title":"When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling","created":"2025-10-17","cited_by_count":3,"area":null},{"id":"2602.01075","title":"ConvexBench: Can LLMs Recognize Convex Functions?","created":"2026-02-01","cited_by_count":3,"area":null},{"id":"2602.11424","title":"Gradients Must Earn Their Influence: Unifying SFT with Generalized Entropic Objectives","created":"2026-02-11","cited_by_count":3,"area":null},{"id":"2603.09065","title":"Learning Adaptive LLM Decoding","created":"2026-03-10","cited_by_count":3,"area":null},{"id":"2603.19470","title":"Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL","created":"2026-03-19","cited_by_count":3,"area":null},{"id":"2605.00015","title":"TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning","created":"2026-04-18","cited_by_count":3,"area":null},{"id":"2605.13467","title":"PDCR: Perception-Decomposed Confidence Reward for Vision-Language Reasoning","created":"2026-05-13","cited_by_count":3,"area":null},{"id":"2608.02181","title":"Start Classifying: Categorical Critics for LLM Reinforcement Learning","created":"2026-08-03","cited_by_count":3,"area":null},{"id":"2412.11699","title":"CoinMath: Harnessing the Power of Coding Instruction for Math LLMs","created":"2024-12-16","cited_by_count":2,"area":null},{"id":"2503.15807","title":"Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture","created":"2025-03-20","cited_by_count":2,"area":null},{"id":"2506.21220","title":"Complexity-aware fine-tuning","created":"2025-06-26","cited_by_count":2,"area":null},{"id":"2507.08425","title":"A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities","created":"2025-07-11","cited_by_count":2,"area":null},{"id":"2508.18473","title":"Principled Detection of Hallucinations in Large Language Models via Multiple Testing","created":"2025-08-25","cited_by_count":2,"area":null},{"id":"2510.03691","title":"REG: A Regularization Optimizer for Robust Training Dynamics","created":"2025-10-04","cited_by_count":2,"area":null},{"id":"2510.18383","title":"MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation","created":"2025-10-21","cited_by_count":2,"area":null},{"id":"2510.23652","title":"The Structural Scalpel: Automated Contiguous Layer Pruning for Large Language Models","created":"2025-10-25","cited_by_count":2,"area":null},{"id":"2510.25770","title":"E-Scores for (In)Correctness Assessment of Generative Model Outputs","created":"2025-10-29","cited_by_count":2,"area":null},{"id":"2511.06430","title":"CG-TTRL: Context-Guided Test-Time Reinforcement Learning for On-Device Large Language Models","created":"2025-11-09","cited_by_count":2,"area":null},{"id":"2602.18292","title":"Decoding as Optimisation on the Probability Simplex: From Top-K to Top-P (Nucleus) to Best-of-K Samplers","created":"2026-02-20","cited_by_count":2,"area":null},{"id":"2603.02775","title":"From Solver to Tutor: Evaluating the Pedagogical Intelligence of LLMs with KMP-Bench","created":"2026-03-03","cited_by_count":2,"area":null},{"id":"2604.16995","title":"SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models","created":"2026-04-18","cited_by_count":2,"area":null},{"id":"2605.11209","title":"Measuring Five-Nines Reliability: Sample-Efficient LLM Evaluation in Saturated Benchmarks","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2605.28631","title":"Single-Rollout Hidden-State Dynamics for Training-Free RLVR Data Selection","created":"2026-05-27","cited_by_count":2,"area":null},{"id":"2605.30859","title":"DARTS: Distribution-Aware Active Rollout Trajectory Shaping for Accelerating LLM Reinforcement Learning","created":"2026-05-29","cited_by_count":2,"area":null}],"next_cursor":"100"}