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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

The research introduces a method called Planning Experience Exploration and Utilization (PEEU) to improve the task planning capabilities of smaller open-source Multi-Language Model Learning Machines (MLLMs). These machines are used for assisting humans with repetitive Graphical User Interface (GUI) tasks but lack effective task planning, cross-website generalization, and high level planning competence. The PEEU method uses autonomous exploration to discover experiences and utilizes hindsight experience to create training data. The study also proposes a Task Decomposition Hierarchical Analysis Framework (TDHAF) for analyzing the generalization behaviors of the model. Results show that the proposed 7B model outperforms a larger Qwen2.5-VL-32B model in real-world benchmarks.

What the Document Says

  • The research presents the Planning Experience Exploration and Utilization (PEEU) method to improve the task planning of smaller MLLMs.
  • MLLMs are used for GUI tasks but suffer from weak planning, limited cross-website generalization, and insufficient high level planning competence.
  • PEEU uses autonomous exploration to discover experiences and hindsight experience utilization for creating training data.
  • A Task Decomposition Hierarchical Analysis Framework (TDHAF) is proposed for analyzing the generalization behaviors of the model.
  • The study reveals that mastering low-level atomic skills does not ensure high-level planning competence, while high-level task training improves Out-Of-Distribution (OOD) generalization.
  • Experiments on real-world benchmarks show PEEU's superior effectiveness with a 7B model outperforming the Qwen2.5-VL-32B model.

Key Concepts Mentioned

  • Multimodal web agents
  • GUI tasks
  • task planning
  • MLLMs
  • PEEU method
  • autonomous exploration
  • hindsight experience utilization
  • TDHAF
  • task decomposition
  • generalization behaviors
  • Out-Of-Distribution (OOD) generalization

Claims Made by the Document

  • Small MLLMs are cost efficient and privacy preserving but suffer from weak planning and limited cross website generalization.
  • PEEU can improve the task planning capabilities of smaller MLLMs.
  • Mastering low level atomic skills does not guarantee high level planning competence.
  • High level task training yields stronger Out-Of-Distribution (OOD) generalization.

Evidence Used

  • The text states that small open source MLLMs suffer from weak planning and limited cross website generalization compared with commercial large models.
  • The text describes the PEEU method as a solution to address these limitations.
  • The text mentions that mastering low-level atomic skills does not ensure high-level planning competence, while high-level task training improves OOD generalization.

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.