{"total":73,"rows":[{"id":"2412.15115","title":"Qwen2.5 Technical Report","created":"2024-12-19","cited_by_count":4942,"area":"moe"},{"id":"2407.10671","title":"Qwen2 Technical Report","created":"2024-07-15","cited_by_count":2120,"area":null},{"id":"2507.20534","title":"Kimi K2: Open Agentic Intelligence","created":"2025-07-28","cited_by_count":567,"area":"agents"},{"id":"2504.21776","title":"WebThinker: Empowering Large Reasoning Models with Deep Research Capability","created":"2025-04-30","cited_by_count":335,"area":"lr"},{"id":"2407.01284","title":"We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?","created":"2024-07-01","cited_by_count":286,"area":"lr"},{"id":"2510.07743","title":"OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment","created":"2025-10-09","cited_by_count":132,"area":"alignment"},{"id":"2507.18624","title":"Checklists Are Better Than Reward Models For Aligning Language Models","created":"2025-07-24","cited_by_count":128,"area":"alignment"},{"id":"2508.14704","title":"MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers","created":"2025-08-20","cited_by_count":96,"area":"wm"},{"id":"2511.16043","title":"Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning","created":"2025-11-20","cited_by_count":75,"area":"lr"},{"id":"2602.01511","title":"Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training","created":"2026-02-02","cited_by_count":54,"area":"alignment"},{"id":"2604.18292","title":"Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence","created":"2026-04-20","cited_by_count":54,"area":"agents"},{"id":"2412.14135","title":"Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective","created":"2024-12-18","cited_by_count":50,"area":"lr"},{"id":"2502.14948","title":"Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation","created":"2025-02-20","cited_by_count":35,"area":"synth"},{"id":"2502.16906","title":"AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models","created":"2025-02-24","cited_by_count":31,"area":"lr"},{"id":"2406.18676","title":"Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation","created":"2024-06-26","cited_by_count":27,"area":"rag"},{"id":"2410.15595","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","created":"2024-10-21","cited_by_count":27,"area":"lr"},{"id":"2507.08649","title":"Leanabell-Prover-V2: Verifier-integrated Reasoning for Formal Theorem Proving via Reinforcement Learning","created":"2025-07-11","cited_by_count":27,"area":"lr"},{"id":"2510.07181","title":"TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics","created":"2025-10-08","cited_by_count":27,"area":"lr"},{"id":"2511.04460","title":"V-Thinker: Interactive Thinking with Images","created":"2025-11-06","cited_by_count":26,"area":"lr"},{"id":"2410.23933","title":"Language Models can Self-Lengthen to Generate Long Texts","created":"2024-10-31","cited_by_count":24,"area":null},{"id":"2412.11605","title":"SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models","created":"2024-12-16","cited_by_count":23,"area":"lr"},{"id":"2412.11919","title":"RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation","created":"2024-12-16","cited_by_count":20,"area":null},{"id":"2407.04078","title":"DotaMath: Decomposition of Thought with Code Assistance and Self-correction for Mathematical Reasoning","created":"2024-07-04","cited_by_count":19,"area":"lr"},{"id":"2506.01413","title":"Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models","created":"2025-06-02","cited_by_count":17,"area":"lr"},{"id":"2410.14208","title":"Montessori-Instruct: Generate Influential Training Data Tailored for Student Learning","created":"2024-10-18","cited_by_count":13,"area":null},{"id":"2512.18160","title":"Propose, Solve, Verify: Self-Play Through Formal Verification","created":"2025-12-20","cited_by_count":13,"area":"synth"},{"id":"2412.13702","title":"Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models","created":"2024-12-18","cited_by_count":12,"area":"mllm"},{"id":"2503.05703","title":"What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces","created":"2025-02-10","cited_by_count":12,"area":null},{"id":"2505.19030","title":"RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data","created":"2025-05-25","cited_by_count":12,"area":null},{"id":"2608.19197","title":"SPADE: Self-Play in Adaptive Synthetic Executable Environments","created":"2026-08-19","cited_by_count":12,"area":"synth"},{"id":"2607.22529","title":"Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills","created":"2026-07-24","cited_by_count":10,"area":"synth"},{"id":"2409.18216","title":"MMMT-IF: A Challenging Multimodal Multi-Turn Instruction Following Benchmark","created":"2024-09-26","cited_by_count":8,"area":null},{"id":"2510.27072","title":"Towards Understanding Self-play for LLM Reasoning","created":"2025-10-31","cited_by_count":8,"area":"lr"},{"id":"2512.23457","title":"Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following","created":"2025-12-29","cited_by_count":8,"area":"alignment"},{"id":"2502.11525","title":"Beyond Single-Task: Robust Multi-Task Length Generalization for LLMs","created":"2025-02-17","cited_by_count":7,"area":null},{"id":"2505.23816","title":"A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs","created":"2025-05-27","cited_by_count":7,"area":"lr"},{"id":"2410.14940","title":"Baichuan Alignment Technical Report","created":"2024-10-19","cited_by_count":6,"area":"alignment"},{"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":"2508.20907","title":"Quantum Verifiable Rewards for Post-Training Qiskit Code Assistant","created":"2025-08-28","cited_by_count":6,"area":null},{"id":"2604.09813","title":"Controllable and Verifiable Tool-Use Data Synthesis for Agentic Reinforcement Learning","created":"2026-04-10","cited_by_count":6,"area":"agents"},{"id":"2505.14147","title":"SHARP: Synthesizing High-quality Aligned Reasoning Problems for Large Reasoning Models Reinforcement Learning","created":"2025-05-20","cited_by_count":5,"area":"lr"},{"id":"2511.04694","title":"Reasoning Up the Instruction Ladder for Controllable Language Models","created":"2025-10-30","cited_by_count":5,"area":"lr"},{"id":"2605.29156","title":"RUBRIC-ARROW: Alternating Pointwise Rubric Reward Modeling for LLM Post-training in Non-verifiable Domains","created":"2026-05-27","cited_by_count":5,"area":"alignment"},{"id":"2506.21384","title":"Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation","created":"2025-06-26","cited_by_count":4,"area":"rag"},{"id":"2507.21645","title":"Libra: Assessing and Improving Reward Model by Learning to Think","created":"2025-07-29","cited_by_count":4,"area":"lr"},{"id":"2511.01014","title":"IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation","created":"2025-11-02","cited_by_count":4,"area":null},{"id":"2507.08967","title":"Self-Improving Model Steering","created":"2025-07-11","cited_by_count":3,"area":"synth"},{"id":"2508.02150","title":"Beyond the Trade-off: Self-Supervised Reinforcement Learning for Reasoning Models' Instruction Following","created":"2025-08-04","cited_by_count":3,"area":"lr"},{"id":"2510.03879","title":"Adversarial Agent Collaboration for Correctness Improvements of C to Safe Rust Translation","created":"2025-10-04","cited_by_count":3,"area":"agents"},{"id":"2510.23558","title":"ISA-Bench: Benchmarking Instruction Sensitivity for Large Audio Language Models","created":"2025-10-27","cited_by_count":3,"area":null},{"id":"2606.01279","title":"ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment","created":"2026-05-31","cited_by_count":3,"area":"agents"},{"id":"2508.13828","title":"Revisiting RAG Ensemble: A Theoretical and Mechanistic Analysis of Multi-RAG System Collaboration","created":"2025-08-19","cited_by_count":2,"area":"rag"},{"id":"2512.02731","title":"Self-Improving AI Agents through Self-Play","created":"2025-12-02","cited_by_count":2,"area":null},{"id":"2601.18129","title":"Typhoon-S: Minimal Open Post-Training for Sovereign Large Language Models","created":"2026-01-26","cited_by_count":2,"area":null},{"id":"2412.12606","title":"Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models","created":"2024-12-17","cited_by_count":1,"area":"mllm"},{"id":"2505.18011","title":"Training with Pseudo-Code for Instruction Following","created":"2025-05-23","cited_by_count":1,"area":null},{"id":"2505.23223","title":"Daunce: Data Attribution through Uncertainty Estimation","created":"2025-05-29","cited_by_count":1,"area":null},{"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":"2604.13006","title":"One Token Away from Collapse: The Fragility of Instruction-Tuned Helpfulness","created":"2026-04-14","cited_by_count":1,"area":null},{"id":"2604.16322","title":"Steerable Instruction Following Coding Data Synthesis with Actor-Parametric Schema Co-Evolution","created":"2026-02-27","cited_by_count":1,"area":"synth"},{"id":"2606.03968","title":"QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards","created":"2026-06-02","cited_by_count":1,"area":null},{"id":"2606.21678","title":"Decodable but Not Faithful: Coupling Natural-Language Rationales to Programmatic Verifiers","created":"2026-06-19","cited_by_count":1,"area":null},{"id":"2505.12845","title":"Multi-Level Aware Preference Learning: Enhancing RLHF for Complex Multi-Instruction Tasks","created":"2025-05-19","cited_by_count":0,"area":"alignment"},{"id":"2510.17354","title":"Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented Generation","created":"2025-10-20","cited_by_count":0,"area":"rag"},{"id":"2512.19455","title":"SiamGPT: Quality-First Fine-Tuning for Stable Thai Text Generation","created":"2025-12-22","cited_by_count":0,"area":null},{"id":"2601.02201","title":"CORE: Code-based Inverse Self-Training Framework with Graph Expansion for Virtual Agents","created":"2026-01-05","cited_by_count":0,"area":"mllm"},{"id":"2603.29211","title":"Xuanwu: Evolving General Multimodal Models into an Industrial-Grade Foundation for Content Ecosystems","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2605.17278","title":"A2RBench: An Automatic Paradigm for Formally Verifiable Abstract Reasoning Benchmark Generation","created":"2026-05-17","cited_by_count":0,"area":"lr"},{"id":"2608.12426","title":"Large Language Models Can Follow Instructions, But Not Many at Once: Phase Transitions in Compositional Constraint Satisfaction","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2608.29030","title":"Learning to Follow In-Context Watermark Instructions via Self-Distillation","created":"2026-08-29","cited_by_count":0,"area":"safety"},{"id":"2608.29352","title":"Cross-Relational Preference Learning for Better LLM Instruction Following","created":"2026-08-29","cited_by_count":0,"area":"alignment"},{"id":"2609.29109","title":"CounterRoute: Self-Routed Reasoning via Hierarchical Counterfactual Credit Assignment","created":"2026-09-24","cited_by_count":0,"area":"lr"},{"id":"2609.29892","title":"Qwen-Planner-Agent: A Closed-Loop AI-for-AI Framework for Real-World Mobile Planner Agents","created":"2026-09-24","cited_by_count":0,"area":"agents"}],"next_cursor":null}