Building Autonomous GUI Navigation via Agentic-Q Estimation and Step-Wise Policy Optimization
arXiv:2602.13653v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have substantially driven the progress of autonomous agents for Graphical User Interface (GUI). Nevertheless, in real-world applications, GUI agents are often faced with non-stationary environments, leading to high computational costs for data curation and policy optimization. In this report, we introduce a novel MLLM-centered framework for GUI agents, which consists of two components: agentic-Q estimation and step-wise policy optimization. The former one aims to optimize a Q-model that can generate step-wise values to evaluate the contribution of a given action to task completion. The latter one takes step-wise samples from the state-action trajectory as inputs, and optimizes the policy via reinforcement learning with our agentic-Q model. It should be noticed that (i) all state-action trajectories are produced by the policy itself, so that the data collection costs are manageab
arXiv:2602.13653v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have substantially driven the progress of autonomous agents for Graphical User Interface (GUI). Nevertheless, in real-world applications, GUI agents are often faced with non-stationary environments, leading to high computational costs for data curation and policy optimization. In this report, we introduce a novel MLLM-centered framework for GUI agents, which consists of two components: agentic-Q estimation and step-wise policy optimization. The former one aims to optimize a Q-model that can generate step-wise values to evaluate the contribution of a given action to task completion. The latter one takes step-wise samples from the state-action trajectory as inputs, and optimizes the policy via reinforcement learning with our agentic-Q model. It should be noticed that (i) all state-action trajectories are produced by the policy itself, so that the data collection costs are manageable; (ii) the policy update is decoupled from the environment, ensuring stable and efficient optimization. Empirical evaluations show that our framework endows Ovis2.5-9B with powerful GUI interaction capabilities, achieving remarkable performances on GUI navigation and grounding benchmarks and even surpassing contenders with larger scales.
Executive Summary
The article introduces a novel framework for autonomous GUI navigation using Multimodal Large Language Models (MLLMs), focusing on agentic-Q estimation and step-wise policy optimization. This approach aims to address the challenges of non-stationary environments in GUI agents, reducing computational costs and ensuring stable policy optimization. The framework leverages the policy itself for data collection and decouples policy updates from the environment, resulting in efficient and manageable optimization processes. Empirical evaluations demonstrate significant improvements in GUI interaction capabilities, surpassing larger-scale contenders.
Key Points
- ▸ Introduction of a novel MLLM-centered framework for GUI agents.
- ▸ Use of agentic-Q estimation to evaluate action contributions.
- ▸ Step-wise policy optimization for efficient and stable reinforcement learning.
- ▸ Empirical evaluations show remarkable performance in GUI navigation and grounding benchmarks.
Merits
Innovative Framework
The proposed framework is innovative in its approach to GUI navigation, leveraging MLLMs and decoupling policy updates from the environment, which ensures stable and efficient optimization.
Cost-Effective Data Collection
By using the policy itself for data collection, the framework significantly reduces the computational costs associated with data curation and policy optimization.
Empirical Success
The empirical evaluations demonstrate the framework's effectiveness, achieving remarkable performance in GUI navigation and grounding benchmarks, even surpassing larger-scale contenders.
Demerits
Limited Generalizability
The framework's performance is primarily demonstrated on specific benchmarks, and its generalizability to other GUI environments and tasks remains to be thoroughly evaluated.
Complexity in Implementation
The implementation of agentic-Q estimation and step-wise policy optimization may be complex and require significant computational resources, potentially limiting its accessibility.
Dependence on Policy Quality
The effectiveness of the framework is highly dependent on the quality of the initial policy, which may not always be optimal or readily available.
Expert Commentary
The article presents a significant advancement in the field of autonomous GUI navigation, addressing critical challenges associated with non-stationary environments and high computational costs. The innovative use of agentic-Q estimation and step-wise policy optimization demonstrates a robust approach to reinforcement learning, ensuring stable and efficient policy updates. The empirical results are impressive, showcasing the framework's ability to surpass larger-scale contenders in GUI navigation and grounding benchmarks. However, the framework's complexity and dependence on policy quality pose potential limitations that need to be addressed for broader applicability. The article's contributions are timely and relevant, aligning with the growing interest in multimodal large language models and their applications in various domains. Future research should focus on evaluating the framework's generalizability and exploring its potential in other non-stationary environments.
Recommendations
- ✓ Further empirical evaluations should be conducted to assess the framework's performance across a wider range of GUI environments and tasks.
- ✓ Researchers should explore methods to simplify the implementation of the framework, making it more accessible and computationally efficient.