{"list":"other","total":58,"rows":[{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"1707.06347","title":"Proximal Policy Optimization Algorithms","created":"2017-07-20","cited_by_count":15215},{"id":"2201.11903","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","created":"2022-01-28","cited_by_count":12459},{"id":"2402.03300","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","created":"2024-02-05","cited_by_count":9379},{"id":"2210.03629","title":"ReAct: Synergizing Reasoning and Acting in Language Models","created":"2022-10-06","cited_by_count":8695},{"id":"2205.11916","title":"Large Language Models are Zero-Shot Reasoners","created":"2022-05-24","cited_by_count":4374},{"id":"2305.20050","title":"Let's Verify Step by Step","created":"2023-05-31","cited_by_count":3789},{"id":"2204.05862","title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback","created":"2022-04-12","cited_by_count":3720},{"id":"2501.12599","title":"Kimi k1.5: Scaling Reinforcement Learning with LLMs","created":"2025-01-22","cited_by_count":1210},{"id":"2606.19348","title":"DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence","created":"2026-04-26","cited_by_count":1040},{"id":"2306.13649","title":"On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes","created":"2023-06-23","cited_by_count":939},{"id":"2308.10144","title":"ExpeL: LLM Agents Are Experiential Learners","created":"2023-08-20","cited_by_count":876},{"id":"2506.13131","title":"AlphaEvolve: A coding agent for scientific and algorithmic discovery","created":"2025-06-16","cited_by_count":867},{"id":"2504.19413","title":"Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory","created":"2025-04-28","cited_by_count":779},{"id":"2501.17161","title":"SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training","created":"2025-01-28","cited_by_count":672},{"id":"2507.19457","title":"GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning","created":"2025-07-25","cited_by_count":542},{"id":"1611.02779","title":"RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning","created":"2016-11-09","cited_by_count":503},{"id":"2505.06120","title":"LLMs Get Lost In Multi-Turn Conversation","created":"2025-05-09","cited_by_count":429},{"id":"2510.04618","title":"Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models","created":"2025-10-06","cited_by_count":398},{"id":"2602.12670","title":"SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks","created":"2026-02-13","cited_by_count":315},{"id":"2406.11695","title":"Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs","created":"2024-06-17","cited_by_count":291},{"id":"2509.25140","title":"ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory","created":"2025-09-29","cited_by_count":284},{"id":"2402.12875","title":"Chain of Thought Empowers Transformers to Solve Inherently Serial Problems","created":"2024-02-20","cited_by_count":260},{"id":"2506.15841","title":"MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents","created":"2025-06-18","cited_by_count":238},{"id":"2508.19828","title":"Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning","created":"2025-08-27","cited_by_count":237},{"id":"2509.04259","title":"RL's Razor: Why Online Reinforcement Learning Forgets Less","created":"2025-09-04","cited_by_count":176},{"id":"2504.07952","title":"Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory","created":"2025-04-10","cited_by_count":163},{"id":"2511.20857","title":"Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory","created":"2025-11-25","cited_by_count":136},{"id":"2301.08028","title":"A Tutorial on Meta-Reinforcement Learning","created":"2023-01-19","cited_by_count":108},{"id":"2604.02268","title":"SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization","created":"2026-04-02","cited_by_count":94},{"id":"2602.16313","title":"MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks","created":"2026-02-18","cited_by_count":84},{"id":"2505.11942","title":"LifelongAgentBench: Evaluating LLM Agents as Lifelong Learners","created":"2025-05-17","cited_by_count":78},{"id":"2510.12635","title":"Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks","created":"2025-10-14","cited_by_count":66},{"id":"2602.02482","title":"Expanding the Capabilities of Reinforcement Learning via Text Feedback","created":"2026-02-02","cited_by_count":53},{"id":"2602.03587","title":"CL-bench: A Benchmark for Context Learning","created":"2026-02-03","cited_by_count":47},{"id":"1803.01118","title":"Some Considerations on Learning to Explore via Meta-Reinforcement Learning","created":"2018-03-03","cited_by_count":44},{"id":"2407.10930","title":"Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together","created":"2024-07-15","cited_by_count":40},{"id":"2102.02926","title":"Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents","created":"2021-02-04","cited_by_count":36},{"id":"2411.19477","title":"Provable Scaling Laws for the Test-Time Compute of Large Language Models","created":"2024-11-29","cited_by_count":26},{"id":"2604.17308","title":"SkillFlow:Benchmarking Lifelong Skill Discovery and Evolution for Autonomous Agents","created":"2026-04-19","cited_by_count":25},{"id":"2505.17826","title":"Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models","created":"2025-05-23","cited_by_count":19},{"id":"2601.05593","title":"PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning","created":"2026-01-09","cited_by_count":17},{"id":"2606.05661","title":"Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments","created":"2026-06-04","cited_by_count":17},{"id":"2604.12290","title":"Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization","created":"2026-04-14","cited_by_count":16},{"id":"2509.24203","title":"Group-Relative REINFORCE Is Secretly an Off-Policy Algorithm: Demystifying Some Myths About GRPO and Its Friends","created":"2025-09-29","cited_by_count":15},{"id":"2605.30785","title":"Learning Agent-Compatible Context Management for Long-Horizon Tasks","created":"2026-05-29","cited_by_count":15},{"id":"2604.08988","title":"SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment","created":"2026-04-10","cited_by_count":14},{"id":"2511.09586","title":"Environment Scaling for Interactive Agentic Experience Collection: A Survey","created":"2025-11-12","cited_by_count":13},{"id":"2605.10899","title":"RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards","created":"2026-05-11","cited_by_count":12},{"id":"2606.02461","title":"AgentCL: Toward Rigorous Evaluation of Continual Learning in Language Agents","created":"2026-06-01","cited_by_count":9},{"id":"2603.01761","title":"Position: Modular Memory is the Key to Continual Learning Agents","created":"2026-03-02","cited_by_count":8},{"id":"2604.11462","title":"Escaping the Context Bottleneck: Active Context Curation for LLM Agents via Reinforcement Learning","created":"2026-04-13","cited_by_count":8},{"id":"2602.04089","title":"Scaling In-Context Online Learning Capability of LLMs via Cross-Episode Meta-RL","created":"2026-02-03","cited_by_count":7},{"id":"2602.10525","title":"LHAW: Controllable Underspecification for Long-Horizon Tasks","created":"2026-02-11","cited_by_count":6},{"id":"2605.12484","title":"Learning, Fast and Slow: Towards LLMs That Adapt Continually","created":"2026-05-12","cited_by_count":6},{"id":"2502.03752","title":"Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning","created":"2025-02-06","cited_by_count":5},{"id":"2510.25441","title":"Grounded in Reality: Learning and Deploying Proactive LLM from Offline Logs","created":"2025-10-29","cited_by_count":3},{"id":"2606.15306","title":"LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure","created":"2026-06-13","cited_by_count":2}],"next_cursor":null}