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An arXiv preprint introduces OdoBot, a web-agent architecture that builds a behavioral model of a web application from successful task-execution demonstrations to reduce token usage. In experiments on 45 tasks in the Canvas LMS, OdoBot used 44% and 80% fewer tokens than Agent-E and WebVoyager respectively, and it outperformed WebVoyager on task success rate.
The paper advocates incorporating enlarged-model pretraining into structured pruning pipelines and treats the enlarge-and-prune process as a single integrated system. It studies whether pretraining a larger model is worthwhile even if the larger model is never deployed and how to optimize the pipeline for token efficiency.
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