Co-EPG: A Framework for Co-Evolution of Planning and Grounding in Autonomous GUI Agents
This work addresses the challenge of improving GUI agents for automation by introducing a novel co-evolution paradigm, representing a significant advancement over incremental approaches.
The paper tackled the problem of insufficient synergy and data utilization in GUI task automation by proposing Co-EPG, a self-iterative training framework for co-evolving planning and grounding, which outperformed state-of-the-art methods on benchmarks like Multimodal-Mind2Web and AndroidControl after three iterations without external data.
Graphical User Interface (GUI) task automation constitutes a critical frontier in artificial intelligence research. While effective GUI agents synergistically integrate planning and grounding capabilities, current methodologies exhibit two fundamental limitations: (1) insufficient exploitation of cross-model synergies, and (2) over-reliance on synthetic data generation without sufficient utilization. To address these challenges, we propose Co-EPG, a self-iterative training framework for Co-Evolution of Planning and Grounding. Co-EPG establishes an iterative positive feedback loop: through this loop, the planning model explores superior strategies under grounding-based reward guidance via Group Relative Policy Optimization (GRPO), generating diverse data to optimize the grounding model. Concurrently, the optimized Grounding model provides more effective rewards for subsequent GRPO training of the planning model, fostering continuous improvement. Co-EPG thus enables iterative enhancement of agent capabilities through self-play optimization and training data distillation. On the Multimodal-Mind2Web and AndroidControl benchmarks, our framework outperforms existing state-of-the-art methods after just three iterations without requiring external data. The agent consistently improves with each iteration, demonstrating robust self-enhancement capabilities. This work establishes a novel training paradigm for GUI agents, shifting from isolated optimization to an integrated, self-driven co-evolution approach.