Summary of Openwebvoyager: Building Multimodal Web Agents Via Iterative Real-world Exploration, Feedback and Optimization, by Hongliang He et al.
OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization
by Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Hongming Zhang, Tianqing Fang, Zhenzhong Lan, Dong Yu
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an open-source framework for developing multimodal web agents that can autonomously explore and improve themselves in real-world scenarios. It trains a base model using imitation learning to gain basic abilities, then allows the agent to collect feedback on its trajectories while exploring the open web. The agent further improves its policy by learning from well-performing trajectories judged by another general-purpose model, repeating this process several times. Experimental results show that the agent successfully improves itself after each iteration, achieving strong performance across multiple test sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way for computer agents to learn and get better at exploring websites on their own. This is different from earlier attempts that only worked in fake environments with clear rules. The new framework lets the agent collect feedback as it explores real websites and then uses this feedback to improve its skills. It’s like learning how to ride a bike – you start by copying what others do, then you try it yourself and get feedback, and finally you become more skilled. This process can be repeated many times, making the agent better and better at navigating websites. |
