{"total":48,"rows":[{"id":"2504.13958","title":"ToolRL: Reward is All Tool Learning Needs","created":"2025-04-16","cited_by_count":362,"area":"agents"},{"id":"2509.02547","title":"The Landscape of Agentic Reinforcement Learning for LLMs: A Survey","created":"2025-09-02","cited_by_count":205,"area":"agents"},{"id":"2511.10395","title":"AgentEvolver: Towards Efficient Self-Evolving Agent System","created":"2025-11-13","cited_by_count":85,"area":"agents"},{"id":"2509.21240","title":"Tree Search for LLM Agent Reinforcement Learning","created":"2025-09-25","cited_by_count":79,"area":"agents"},{"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":"2510.14967","title":"Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn Search Agents","created":"2025-10-16","cited_by_count":48,"area":"agents"},{"id":"2511.16108","title":"SkyRL-Agent: Efficient RL Training for Multi-turn LLM Agent","created":"2025-11-20","cited_by_count":48,"area":"agents"},{"id":"2508.18669","title":"MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use","created":"2025-08-26","cited_by_count":32,"area":"agents"},{"id":"2602.22897","title":"OmniGAIA: Towards Native Omni-Modal AI Agents","created":"2026-02-26","cited_by_count":22,"area":"agents"},{"id":"2605.07725","title":"SOD: Step-wise On-policy Distillation for Small Language Model Agents","created":"2026-05-08","cited_by_count":21,"area":"agents"},{"id":"2603.18815","title":"ProRL Agent: Rollout-as-a-Service for RL Training of Multi-Turn LLM Agents","created":"2026-03-19","cited_by_count":20,"area":"agents"},{"id":"2510.10197","title":"Don't Just Fine-tune the Agent, Tune the Environment","created":"2025-10-11","cited_by_count":19,"area":"agents"},{"id":"2607.22688","title":"Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents","created":"2026-07-17","cited_by_count":18,"area":"agents"},{"id":"2510.04678","title":"Multi-Agent Tool-Integrated Policy Optimization","created":"2025-10-06","cited_by_count":16,"area":"agents"},{"id":"2506.21669","title":"SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents","created":"2025-06-26","cited_by_count":15,"area":"agents"},{"id":"2509.18847","title":"Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions","created":"2025-09-23","cited_by_count":12,"area":"agents"},{"id":"2505.20128","title":"Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers","created":"2025-05-26","cited_by_count":11,"area":"agents"},{"id":"2508.00344","title":"PilotRL: Training Language Model Agents via Global Planning-Guided Progressive Reinforcement Learning","created":"2025-08-01","cited_by_count":11,"area":"agents"},{"id":"2509.22601","title":"Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning","created":"2025-09-26","cited_by_count":11,"area":"agents"},{"id":"2602.13691","title":"PhGPO: Pheromone-Guided Policy Optimization for Long-Horizon Tool Planning","created":"2026-02-14","cited_by_count":9,"area":"agents"},{"id":"2605.17734","title":"Harnessing LLM Agents with Skill Programs","created":"2026-05-18","cited_by_count":9,"area":"agents"},{"id":"2510.16720","title":"Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AI","created":"2025-10-19","cited_by_count":8,"area":"agents"},{"id":"2603.24943","title":"FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol","created":"2026-03-26","cited_by_count":8,"area":"agents"},{"id":"2509.25779","title":"Planner-R1: Reward Shaping Enables Efficient Agentic RL with Smaller LLMs","created":"2025-09-30","cited_by_count":7,"area":"agents"},{"id":"2512.10534","title":"Achieving Olympiad-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning","created":"2025-12-11","cited_by_count":7,"area":"agents"},{"id":"2605.00737","title":"To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling","created":"2026-05-01","cited_by_count":7,"area":"agents"},{"id":"2606.26027","title":"Why Multi-Step Tool-Use Reinforcement Learning Collapses and How Supervisory Signals Fix It","created":"2026-06-24","cited_by_count":7,"area":"agents"},{"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":"2510.19286","title":"TheMCPCompany: Creating General-purpose Agents with Task-specific Tools","created":"2025-10-22","cited_by_count":4,"area":"agents"},{"id":"2601.15141","title":"CLEANER: Self-Purified Trajectories Boost Agentic Reinforcement Learning","created":"2026-01-21","cited_by_count":4,"area":"agents"},{"id":"2606.12384","title":"APPO: Agentic Procedural Policy Optimization","created":"2026-06-10","cited_by_count":4,"area":"agents"},{"id":"2608.13179","title":"Teach the Magnitude, Not the Direction: Verifier-Bounded Credit Assignment for Multi-Turn Multi-step LLM Agents","created":"2026-08-13","cited_by_count":4,"area":"agents"},{"id":"2509.14718","title":"ToolSample: Dual Dynamic Sampling Methods with Curriculum Learning for RL-based Tool Learning","created":"2025-09-18","cited_by_count":3,"area":"agents"},{"id":"2606.31650","title":"ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL","created":"2026-06-30","cited_by_count":3,"area":"agents"},{"id":"2608.21156","title":"Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence","created":"2026-08-21","cited_by_count":3,"area":"agents"},{"id":"2607.12042","title":"SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning","created":"2026-07-13","cited_by_count":2,"area":"agents"},{"id":"2603.16876","title":"Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation","created":"2026-02-17","cited_by_count":1,"area":"agents"},{"id":"2606.03762","title":"Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning","created":"2026-06-02","cited_by_count":1,"area":"agents"},{"id":"2609.05576","title":"EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent","created":"2026-09-04","cited_by_count":1,"area":"agents"},{"id":"2605.08399","title":"CoCoDA: Co-evolving Compositional DAG for Tool-Augmented Agents","created":"2026-05-08","cited_by_count":0,"area":"agents"},{"id":"2606.06835","title":"Translate-R1: Cost-Aware Translation Tool Use via Reinforcement Learning","created":"2026-06-05","cited_by_count":0,"area":"agents"},{"id":"2606.11652","title":"IAPO: Input Attribution-Aware Policy Optimization for Tool Use in Small Multimodal Agents","created":"2026-06-10","cited_by_count":0,"area":"agents"},{"id":"2607.04713","title":"RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents","created":"2026-07-06","cited_by_count":0,"area":"agents"},{"id":"2607.07178","title":"Entropy Pacing Policy Optimization for Multi-Task Agentic Reinforcement Learning","created":"2026-07-08","cited_by_count":0,"area":"agents"},{"id":"2608.06128","title":"Contextual Information Policy Optimization for Search Agents","created":"2026-08-06","cited_by_count":0,"area":"agents"},{"id":"2608.07437","title":"Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing","created":"2026-08-07","cited_by_count":0,"area":"agents"},{"id":"2609.34225","title":"USA: Update-aware SAM for Cross-domain On-Policy Disitllation of Language Agents","created":"2026-09-28","cited_by_count":0,"area":"agents"},{"id":"2610.11171","title":"VAMR: Multi-Question Agentic Reasoning for Efficient Long-Form Video Understanding","created":"2026-10-08","cited_by_count":0,"area":"agents"}],"next_cursor":null}