Continual GUI Agents
As digital environments (data distribution) are in flux, with new GUI data arriving over time-introducing new domains or resolutions-agents trained on static environments deteriorate in performance. In this work, we introduce Continual GUI Agents, a new task that requires GUI agents to perform continual learning under shifted domains and resolutions. We find existing methods fail to maintain stable grounding as GUI distributions shift over time, due to the diversity of UI interaction points and regions in fluxing scenarios. To address this, we introduce GUI-Anchoring in Flux (GUI-AiF), a new reinforcement fine-tuning framework that stabilizes continual learning through two novel rewards: Anchoring Point Reward in Flux (APR-iF) and Anchoring Region Reward in Flux (ARR-iF). These rewards guide the agents to align with shifting interaction points and regions, mitigating the tendency of existing reward strategies to over-adapt to static grounding cues (e.g., fixed coordinates or element scales). Extensive experiments show GUI-AiF surpasses state-of-the-art baselines. Our work establishes the first continual learning framework for GUI agents, revealing the untapped potential of reinforcement fine-tuning for continual GUI Agents.
69804c686676f933227066bd
Ziwei Liu
69804c686676f933227066be
Borui Kang
69804c686676f933227066bf
Hangjie Yuan
claimed_verified
2026-02-02T16:52:12.133Z
649d54b314afbb10ce2a9eeb
/avatars/15c325d8c2273ff63569f23015e98486.svg
Hangjie Yuan
JacobYuan
user
JacobYuan
69804c686676f933227066c0
Zixiang Zhao
69804c686676f933227066c1
Wei Li
69804c686676f933227066c2
Yifan Zhu
69804c686676f933227066c3
Tao Feng
Continual GUI Agents
As digital environments (data distribution) are in flux, with new GUI data arriving over time-introducing new domains or resolutions-agents trained on static environments deteriorate in performance. In this work, we introduce Continual GUI Agents, a new task that requires GUI agents to perform continual learning under shifted domains and resolutions. We find existing methods fail to maintain stable grounding as GUI distributions shift over time, due to the diversity of UI interaction points and regions in fluxing scenarios. To address this, we introduce GUI-Anchoring in Flux (GUI-AiF), a new reinforcement fine-tuning framework that stabilizes continual learning through two novel rewards: Anchoring Point Reward in Flux (APR-iF) and Anchoring Region Reward in Flux (ARR-iF). These rewards guide the agents to align with shifting interaction points and regions, mitigating the tendency of existing reward strategies to over-adapt to static grounding cues (e.g., fixed coordinates or element scales). Extensive experiments show GUI-AiF surpasses state-of-the-art baselines. Our work establishes the first continual learning framework for GUI agents, revealing the untapped potential of reinforcement fine-tuning for continual GUI Agents.
Continual GUI Agents framework addresses performance degradation in dynamic digital environments through reinforcement fine-tuning with novel anchoring rewards that stabilize learning across shifting UI domains and resolutions.
continual learning GUI agents reinforcement fine-tuning GUI-Anchoring in Flux APR-iF ARR-iF continual GUI Agents