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Source note3 min read

Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning

Source

arXiv

arxiv.org/abs/2606.27330v1

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Plain-Language Brief

Researchers have developed a method called Planning Experience Exploration and Utilization (PEEU) to improve the task planning capabilities of small open-source multi-modal web agents. These agents assist humans in operating repetitive Graphical User Interface (GUI) tasks, but often struggle with weak planning and limited cross-website generalization compared to larger, commercial models. The PEEU method autonomously explores environments to discover experiences and utilizes hindsight experience to synthesize high-level training data. A new analysis framework called Task Decomposition Hierarchical Analysis Framework (TDHAF) was proposed to study compositional generalization across different task granularities. The results show that PEEU significantly outperforms a larger model on real-world benchmarks.

What the Document Says

  • The paper discusses the challenges faced by small open source MLLMs in effective task planning for multimodal web agents that assist humans with repetitive GUI tasks.
  • To address these issues, a method called PEEU (Planning Experience Exploration and Utilization) is introduced to autonomously explore environments, discover experiences, and utilize hindsight experience to synthesize high-level training data.
  • The TDHAF framework was proposed for quantitative analysis of the generalization behaviors that drive this performance.
  • Analysis using TDHAF reveals that mastering low level atomic skills does not guarantee high level planning competence, while high level task training yields stronger Out-of-Distribution (OOD) generalization.
  • Experiments on real-world benchmarks demonstrate the superior effectiveness of PEEU: a 7B model achieves 30.6% accuracy, outperforming the Qwen2.5-VL-32B model.

Key Concepts Mentioned

  • Multimodal web agents
  • GUI tasks
  • task planning
  • small open source MLLMs
  • commercial large models
  • PEEU method
  • Planning Experience Exploration and Utilization
  • TDHAF framework
  • Task Decomposition Hierarchical Analysis Framework
  • Out-of-Distribution (OOD) generalization

Claims Made by the Document

  • Small open source MLLMs struggle with weak planning and limited cross website generalization compared to larger, commercial models.
  • PEEU method addresses these limitations by autonomously exploring environments and utilizing hindsight experience for high-level training data synthesis.
  • TDHAF framework was proposed for quantitative analysis of the generalization behaviors driving PEEU's performance.
  • Mastering low level atomic skills does not guarantee high level planning competence, while high level task training yields stronger OOD generalization.
  • PEEU outperforms a larger model on real-world benchmarks.

Evidence Used

  • The text describes the challenges faced by small open source MLLMs and presents the PEEU method as a solution.
  • The TDHAF framework is introduced as a tool for analyzing the generalization behaviors driving PEEU's performance.
  • The analysis results from TDHAF are presented, showing the relationship between low level skills, high level planning competence, and OOD generalization.
  • Experiment results on real-world benchmarks comparing PEEU and a larger model are provided.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks into executable actions. While small open source MLLMs are cost efficient and privacy preserving compared with commercial large models, they suffer from weak planning and limited cross website generalization. To address these limitations, we introduce the planning experience exploration and utilization (PEEU) method, which autonomously explores environments to discover experiences and utilizes hindsight experience to synthesize strictly aligned, high level training data. To quantitatively analyze the generalization behaviors driving this performance, we propose the task decomposition hierarchical analysis framework (TDHAF) to systematically study compositional generalization across three task granularities: low, middle and high levels. Our analysis reveals that mastering low level atomic skills does not guarantee high level planning competence, while high level task training yields stronger OOD generalization. Experiments on real world benchmarks demonstrate PEEU's superior effectiveness: our 7B model achieves 30.6% accuracy, outperforming the much larger Qwen2.5-VL-32B model. These demonstrate constructing hindsight high level tasks and leveraging experiences is crucial for OOD planning abilities of small MLLMs.