HiconAgent: History Context-aware Policy Optimization for GUI Agents
Graphical User Interface (GUI) agents require effective use of historical context to perform sequential navigation tasks. While incorporating past actions and observations can improve decision making, naive use of full history leads to excessive computational overhead and distraction from irrelevant information. To address this, we introduce HiconAgent, a GUI agent trained with History Context-aware Policy Optimization (HCPO) for efficient and effective utilization of historical information. HCPO optimizes history usage in both sampling and policy updates through two complementary components: (1) Dynamic Context Sampling (DCS) presents the agent with variable length histories during sampling, enabling adaptive use of the most relevant context; (2) Anchor-guided History Compression (AHC) refines the policy update phase with a dual branch strategy where the compressed branch removes history observations while keeping history actions as information flow anchors. The compressed and uncompressed branches are coupled through a history-enhanced alignment loss to enforce consistent history usage while maintaining efficiency. Experiments on mainstream GUI navigation benchmarks demonstrate strong performance. Despite being smaller, HiconAgent-3B outperforms GUI-R1-7B by +8.46 percent grounding accuracy and +11.32 percent step success rate on GUI-Odyssey, while achieving comparable results on AndroidControl and AITW with up to 2.47x computational speedup and 60 percent FLOPs reduction.
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Xurui Zhou
claimed_verified
2025-12-03T09:18:11.631Z
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ZhouXuRui
Minuskid
user
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Gongwei Chen
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Yuquan Xie
claimed_verified
2025-12-03T09:18:09.622Z
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Yuquan Xie
xieyuquan
user
xieyuquan
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Zaijing Li
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Kaiwen Zhou
692e651037312eaa83fd880b
Shuai Wang
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Shuo Yang
692e651037312eaa83fd880d
Zhuotao Tian
692e651037312eaa83fd880e
Rui Shao
HiconAgent: History Context-aware Policy Optimization for GUI Agents
Graphical User Interface (GUI) agents require effective use of historical context to perform sequential navigation tasks. While incorporating past actions and observations can improve decision making, naive use of full history leads to excessive computational overhead and distraction from irrelevant information. To address this, we introduce HiconAgent, a GUI agent trained with History Context-aware Policy Optimization (HCPO) for efficient and effective utilization of historical information. HCPO optimizes history usage in both sampling and policy updates through two complementary components: (1) Dynamic Context Sampling (DCS) presents the agent with variable length histories during sampling, enabling adaptive use of the most relevant context; (2) Anchor-guided History Compression (AHC) refines the policy update phase with a dual branch strategy where the compressed branch removes history observations while keeping history actions as information flow anchors. The compressed and uncompressed branches are coupled through a history-enhanced alignment loss to enforce consistent history usage while maintaining efficiency. Experiments on mainstream GUI navigation benchmarks demonstrate strong performance. Despite being smaller, HiconAgent-3B outperforms GUI-R1-7B by +8.46 percent grounding accuracy and +11.32 percent step success rate on GUI-Odyssey, while achieving comparable results on AndroidControl and AITW with up to 2.47x computational speedup and 60 percent FLOPs reduction.
HiconAgent, a GUI agent using HCPO, efficiently utilizes historical context for better performance in navigation tasks with reduced computational cost.
GUI agents History Context-aware Policy Optimization (HCPO) Dynamic Context Sampling (DCS) Anchor-guided History Compression (AHC) history-enhanced alignment loss GUI navigation benchmarks grounding accuracy step success rate computational speedup FLOPs reduction