arXiv:2606.17871v1 Announce Type: new Abstract: Web navigation requires agents to follow natural language goals, interact with web pages, and produce accurate answers. While recent advances leverage vision-language models and reinforcement learning, existing methods still suffer from single-step fragility due to reward misalignment and error propagation. To tackle the reward entanglement, we design Dynamic Dual-Policy Optimization (DDPO), which dynamically switches between a navigation-first mode for exploration and an answer-first mode for question-answering to mitigate reward conflict. To ca
Advances in AI, particularly Large Language Models and reinforcement learning, are pushing the boundaries of autonomous agents, making solutions to past fragility more pressing and achievable.
This development addresses key limitations in AI agent reliability for complex web interactions, critical for expanding their utility in automated workflows and information gathering.
AI agents can now more robustly handle reward misalignment and error propagation in multi-step web tasks, leading to more dependable autonomous web navigation and interaction.
- · AI software developers
- · Businesses adopting automation
- · Users of AI-driven web services
- · Tasks requiring manual web navigation
- · Companies relying on outdated automation software
Improved reliability of autonomous AI agents for web-based tasks and data extraction.
Accelerated adoption of AI agents across various industries for information retrieval and task automation, reducing human intervention.
Enhanced AI agent capabilities could lead to more sophisticated automated cyber operations, both beneficial and potentially malicious.
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Read at arXiv cs.AI