mlsanityData to 14 Oct 2026

Rethinking Agentic Reinforcement Learning In Large Language Models

arXiv 2604.27859 ↗First posted 30 Apr 2026cs.AI cs.ETcited by 1

On the map: LLM reasoning → Reasoning for agents, planning and code

also in 1 more
Abstract (arXiv)

Reinforcement Learning (RL) has traditionally focused on training specialized agents to optimize predefined reward functions within narrowly defined environments. However, the advent of powerful Large Language Models (LLMs) and increasingly complex, open-ended tasks has catalyzed a paradigm shift towards agentic paradigms within RL. This emerging framework extends beyond traditional RL by emphasizing the development of autonomous agents capable of goal-setting, long-term planning, dynamic strategy adaptation, and interactive reasoning in uncertain, real-world environments. Unlike conventional approaches that rely heavily on static objectives and episodic interactions, LLM-based Agentic RL incorporates cognitive-like capabilities such as meta-reasoning, self-reflection, and multi-step decision-making directly into the learning loop. In this paper, we provide a deep insight for looking the conceptual foundations, methodological innovations, and effective designs underlying this trend. Furthermore, we identify critical challenges and outline promising future directions for building LLM-based Agentic RL.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 97 of its 131 references that are in the map. First posted on arXiv 30 Apr 2026.

Builds on

97 of 131 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025Demystifying Reinforcement Learning in Agentic Reasoning13 Oct
  2. 2025Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents3 Sep
  3. 2025MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents18 Jun
  4. 2025Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering29 May
  5. 2025R1-Code-Interpreter: LLMs Reason with Code via Supervised and Multi-stage Reinforcement Learning27 May
  6. 2025Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving12 May
  7. 2025ReTool: Reinforcement Learning for Strategic Tool Use in LLMs15 Apr

Other references · 90

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  5. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
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  7. 2025Group Sequence Policy Optimizationcited by 835
  8. 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memorycited by 779
  9. 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agentscited by 610
  10. 2025The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Searchcited by 422
  11. 2024Understanding the planning of LLM agents: A surveycited by 421
  12. 2024Hallucination of Multimodal Large Language Models: A Surveycited by 388
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  3. 2025DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decompositioncited by 287
  4. 2025VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Taskscited by 285
  5. 2023FireAct: Toward Language Agent Fine-tuningcited by 256
  6. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 249
  7. 2025GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agentscited by 244
  8. 2025Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learningcited by 237
  9. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 205
  10. 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 205
  11. 2024VinePPO: Refining Credit Assignment in RL Training of LLMscited by 183
  12. 2025A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligencecited by 170
  13. 2024The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Surveycited by 168
  14. 2025UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learningcited by 168
  15. 2025SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agentscited by 142
  16. 2025InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasonerscited by 140
  17. 2023GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigationcited by 137
  18. 2025From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMscited by 124
  19. 2025WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agentcited by 119
  20. 2025Geometric-Mean Policy Optimizationcited by 111
  21. 2025Agentic Large Language Models, a surveycited by 108
  22. 2025A Survey on Code Generation with LLM-based Agentscited by 106
  23. 2025Perception-Aware Policy Optimization for Multimodal Reasoningcited by 99
  24. 2025Soft Adaptive Policy Optimizationcited by 96
  25. 2024Reinforcement Learning for LLM Post-Training: A Surveycited by 92
  26. 2024A Survey on Self-Evolution of Large Language Modelscited by 91
  27. 2026OpenClaw-RL: Train Any Agent Simply by Talkingcited by 90
  28. 2024Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Surveycited by 83
  29. 2025Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judgecited by 76
  30. 2024Reinforcement Learning Enhanced LLMs: A Surveycited by 71
  31. 2025Are Reasoning Models More Prone to Hallucination?cited by 64
  32. 2025A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challengescited by 62
  33. 2025TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modelingcited by 61
  34. 2025G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systemscited by 59
  35. 2025A Survey on Post-training of Large Language Modelscited by 54
  36. 2025MobileGUI-RL: Advancing Mobile GUI Agent through Reinforcement Learning in Online Environmentcited by 52
  37. 2025Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Managementcited by 51
  38. 2025Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Modelscited by 44

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Prospective Hindsight: Self-Calibrating Reinforcement Learning via Prediction-Reality Gapscited by 0

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  3. 2026Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks
  4. 2025Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle
  5. 2024On the Modeling Capabilities of Large Language Models for Sequential Decision Making
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  8. 2025Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AI