Summary of Autonomous Evaluation and Refinement Of Digital Agents, by Jiayi Pan et al.
Autonomous Evaluation and Refinement of Digital Agents
by Jiayi Pan, Yichi Zhang, Nicholas Tomlin, Yifei Zhou, Sergey Levine, Alane Suhr
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 presents a novel approach to improving the performance of digital agents for web navigation and device control using domain-general automatic evaluators. The authors experiment with various evaluation models that balance inference cost, design modularity, and accuracy. They validate their findings on popular benchmarks for digital agents, achieving high agreement with oracle metrics. By fine-tuning and providing guidance at inference time, the authors demonstrate significant improvements in performance, boosting state-of-the-art results by 29% on WebArena and achieving 75% relative improvement in device control settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows that special computer programs can help make smart devices like robots or phones work better. They tested different ways to measure how well these programs do their job, balancing how much time it takes with how accurate they are. The results were impressive, agreeing closely with what experts think is the right answer. By using this program to adjust and guide other computers, they made them perform even better, beating previous records by a lot. |
Keywords
» Artificial intelligence » Boosting » Fine tuning » Inference
