---
title: "AdaPlanner — paper by Sun et al. (2023) — summary and ideas"
description: "LLM agents for sequential decision-making usually either act step by step without any plan or follow a… 23 references, cited by 70 papers here."
authors: ["Haotian Sun", "Yuchen Zhuang", "Lingkai Kong", "Bo Dai", "Chao Zhang"]
year: 2023
venue: "arXiv:2305.16653"
source: "https://arxiv.org/abs/2305.16653"
doi: "10.48550/arxiv.2305.16653"
arxiv: "2305.16653"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sun-2023-adaplanner-adaptive-planning-feedback-language/"
---
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# AdaPlanner: Adaptive Planning from Feedback with Language Models

**Authors:** [Haotian Sun](https://md.research.elamur.ai/free/en/authors/haotian-sun/), [Yuchen Zhuang](https://md.research.elamur.ai/free/en/authors/yuchen-zhuang/), [Lingkai Kong](https://md.research.elamur.ai/free/en/authors/lingkai-kong/), [Bo Dai](https://md.research.elamur.ai/free/en/authors/bo-dai/) and [Chao Zhang](https://md.research.elamur.ai/free/en/authors/chao-zhang/) · **Year:** 2023 · **Venue:** [arXiv:2305.16653](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/) ([NeurIPS 2023](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2023/)) · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2305.16653) · [PDF](https://arxiv.org/pdf/2305.16653) · [DOI 10.48550/arxiv.2305.16653](https://doi.org/10.48550/arxiv.2305.16653) · [arXiv 2305.16653](https://arxiv.org/abs/2305.16653) · [OpenAlex](https://openalex.org/W7133187879)

**Citations:** 23 citations (OpenAlex, 2026-10-14) · [70](https://md.research.elamur.ai/free/en/papers/sun-2023-adaplanner-adaptive-planning-feedback-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLM agents for sequential decision-making usually either act step by step without any plan or follow a fixed plan, adjusting only the current action when the environment responds. Their performance drops as tasks grow longer and more complex. DEPS does revise whole plans, but it needs a trained plan selector and task-specific data. AdaPlanner is an explicit closed-loop method that works only through prompting. A single LLM acts as both planner and refiner. The planner splits a task into sub-goals and predicts what the environment should return. If an observation matches the prediction, the agent calls ask_LLM() to extract useful details and continues. If it contradicts the prediction, the plan is rewritten and execution resumes from the current point rather than restarting. Plans are written as code-style prompts to reduce hallucination. A skill memory stores successful plans and reuses them as few-shot examples. On ALFWorld and MiniWoB++, AdaPlanner beats state-of-the-art baselines while using far fewer demonstrations. This suggests that adaptive replanning makes training-free LLM agents both stronger and more sample-efficient.

## Key points
- AdaPlanner uses two refinement modes. In-plan refinement pulls information from expected observations via ask_LLM(). Out-of-plan refinement revises the whole plan when feedback deviates from the prediction.
- It outperforms state-of-the-art baselines by 3.73% on ALFWorld (with 2x fewer samples) and by 4.11% on MiniWoB++ (with 600x fewer samples).
- Experiments use GPT-3 and GPT-3.5, with Reflexion among the compared baselines. The ALFWorld evaluation covers 134 tasks.
- In ablations, skill discovery nearly doubles the ALFWorld success rate and raises MiniWoB++ success by about 15%. Removing the code interface also lowers performance.
- The authors note that complex tasks still require few-shot expert demonstrations. Code is released at https://github.com/haotiansun14/AdaPlanner.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai and Chao Zhang. 2023. AdaPlanner: Adaptive Planning from Feedback with Language Models. arXiv:2305.16653.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{sun2023adaplanner,
  title = {{AdaPlanner: Adaptive Planning from Feedback with Language Models}},
  author = {Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai and Chao Zhang},
  year = {2023},
  doi = {10.48550/arxiv.2305.16653},
  eprint = {2305.16653},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.16653}
}
```

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23 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*