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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 introduce a new method called PEEU (Planning Experience Exploration and Utilization) to improve the task planning capabilities of small, open-source Multimodal Language Learning Models (MLLMs). These models are cost-effective and privacy-preserving but suffer from weak planning and limited cross-website generalization. PEEU explores environments to discover experiences and utilizes hindsight experience to synthesize training data. A new framework, TDHAF (Task Decomposition Hierarchical Analysis Framework), is also proposed to analyze the factors affecting this performance. The study reveals that low-level skills do not guarantee high-level planning competence, while high-level task training yields stronger out-of-distribution (OOD) generalization. Experiments show PEEU's effectiveness: a 7B model achieves 30.6% accuracy, outperforming a much larger Qwen2.5-VL-32B model.

What the Document Says

  • The research introduces the Planning Experience Exploration and Utilization (PEEU) method to improve the task planning capabilities of small, open-source Multimodal Language Learning Models (MLLMs).
  • These models suffer from weak planning and limited cross-website generalization.
  • PEEU explores environments to discover experiences and utilizes hindsight experience to synthesize training data.
  • A new framework, TDHAF (Task Decomposition Hierarchical Analysis Framework), is proposed to analyze the factors affecting the performance of PEEU.
  • The study 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 show PEEU's effectiveness: a 7B model achieves 30.6% accuracy, outperforming a much larger Qwen2.5-VL-32B model.

Key Concepts Mentioned

  • Multimodal web agents
  • GUI tasks
  • Task planning
  • Small open source MLLMs
  • Commercial large models
  • Planning Experience Exploration and Utilization (PEEU)
  • Hindsight experience
  • Task Decomposition Hierarchical Analysis Framework (TDHAF)
  • Out-of-distribution (OOD) generalization
  • 7B model
  • Qwen2.5-VL-32B model

Claims Made by the Document

  • PEEU can improve the task planning capabilities of small, open-source MLLMs.
  • High level task training yields stronger OOD generalization.
  • A 7B model outperforms a much larger Qwen2.5-VL-32B model.

Evidence Used

  • Introducing PEEU method
  • PEEU explores environments to discover experiences and utilizes hindsight experience
  • Proposing TDHAF for analyzing factors affecting performance
  • Study results revealing mastering low-level skills vs high-level planning competence
  • Experiments demonstrating PEEU's effectiveness and superiority over Qwen2.5-VL-32B model.

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.