mlsanityData to 14 Oct 2026

AdaPlanner: Adaptive Planning from Feedback with Language Models

arXiv 2305.16653 ↗First posted 26 May 2023cs.CL cs.AI cs.LGcited by 196

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. However, most existing methods either take actions greedily without planning or rely on static plans that are not adaptable to environmental feedback. Consequently, the sequential decision-making performance of LLM agents degenerates with problem complexity and plan horizons increase. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback. In AdaPlanner, the LLM agent adaptively refines its plan from feedback with both in-plan and out-of-plan refinement strategies. To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities. Furthermore, we propose a skill discovery mechanism that leverages successful plans as few-shot exemplars, enabling the agent to plan and refine with fewer task demonstrations. Our experiments in the ALFWorld and MiniWoB++ environments demonstrate that AdaPlanner outperforms state-of-the-art baselines by 3.73% and 4.11% while utilizing 2x and 600x fewer samples, respectively.

Key facts. Cited by 196 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 22 of its 30 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

22 of 30 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents3 Feb

Other references · 21

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  6. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
  7. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,210
  8. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,205
  9. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,190
  10. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,081
  11. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
  12. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,035
9 more
  1. 2022PAL: Program-aided Language Modelscited by 652
  2. 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 611
  3. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  4. 2023Language Models can Solve Computer Taskscited by 372
  5. 2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 356
  6. 2018Reinforcement Learning on Web Interfaces Using Workflow-Guided Explorationcited by 284
  7. 2022TALM: Tool Augmented Language Modelscited by 197
  8. 2023Generalized Planning in PDDL Domains with Pretrained Large Language Modelscited by 139
  9. 2022A data-driven approach for learning to control computerscited by 88

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 24; 2024: 56; 2025: 61; 2026: 55.

196 papers.

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  1. 2023ExpeL: LLM Agents Are Experiential Learnerscited by 876
  2. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Modelscited by 574
  3. 2024Agent Workflow Memorycited by 383
  4. 2023A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesiscited by 353
  5. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 274
  6. 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 257
  7. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 205
  8. 2025Plan-and-Act: Improving Planning of Agents for Long-Horizon Taskscited by 195
  9. 2023Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Controlcited by 187
  10. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  11. 2023JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Modelscited by 171
  12. 2024InfiAgent-DABench: Evaluating Agents on Data Analysis Taskscited by 140
38 more
  1. 2024Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffoldingcited by 117
  2. 2024GUICourse: From General Vision Language Models to Versatile GUI Agentscited by 114
  3. 2024AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agentscited by 114
  4. 2024WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environmentcited by 107
  5. 2024WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Explorationcited by 101
  6. 2024The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Modelscited by 93
  7. 2024AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generationcited by 83
  8. 2025PlanGenLLMs: A Modern Survey of LLM Planning Capabilitiescited by 83
  9. 2024Embodied LLM Agents Learn to Cooperate in Organized Teamscited by 82
  10. 2024EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Recordscited by 78
  11. 2024AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agentscited by 74
  12. 2024A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learningcited by 74
  13. 2025AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learningcited by 72
  14. 2024Agent Planning with World Knowledge Modelcited by 70
  15. 2023LASER: LLM Agent with State-Space Exploration for Web Navigationcited by 66
  16. 2023SteP: Stacked LLM Policies for Web Actionscited by 66
  17. 2024StateFlow: Enhancing LLM Task-Solving through State-Driven Workflowscited by 65
  18. 2023AvatarGPT: All-in-One Framework for Motion Understanding, Planning, Generation and Beyondcited by 63
  19. 2024KnowAgent: Knowledge-Augmented Planning for LLM-Based Agentscited by 61
  20. 2024AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learningcited by 61
  21. 2024HYDRA: Model Factorization Framework for Black-Box LLM Personalizationcited by 60
  22. 2024NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wildcited by 59
  23. 2025Evaluating LLM-based Agents for Multi-Turn Conversations: A Surveycited by 57
  24. 2023Tree-Planner: Efficient Close-loop Task Planning with Large Language Modelscited by 48
  25. 2023Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiencycited by 46
  26. 2024On the Multi-turn Instruction Following for Conversational Web Agentscited by 43
  27. 2024On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)cited by 40
  28. 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 40
  29. 2024Adaptive In-conversation Team Building for Language Model Agentscited by 39
  30. 2024m&m's: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Taskscited by 38
  31. 2026Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharingcited by 36
  32. 2024BAGEL: Bootstrapping Agents by Guiding Exploration with Languagecited by 34
  33. 2024Tool-Planner: Task Planning with Clusters across Multiple Toolscited by 33
  34. 2023An In-depth Survey of Large Language Model-based Artificial Intelligence Agentscited by 31
  35. 2025A Survey of Data Agents: Emerging Paradigm or Overstated Hype?cited by 30
  36. 2024A Survey of Useful LLM Evaluationcited by 29
  37. 2024TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysiscited by 29
  38. 2025LLM-Based Human-Agent Collaboration and Interaction Systems: A Surveycited by 29

50 most cited of 196 All 196 (JSON)

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  1. 2026AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints
  2. 2026From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents
  3. 2025Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents
  4. 2024Query-Efficient Planning with Language Models
  5. 2025LLMs Can Plan Only If We Tell Them
  6. 2026Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents
  7. 2025HiPlan: Hierarchical Planning for LLM-Based Agents with Adaptive Global-Local Guidance
  8. 2026GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI