NNetscape Navigator: Complex Demonstrations for Web Agents Without a Demonstrator
Keywords: language model agents, large language models, demonstrations for sequential decision making, language conditioned RL, grounded instruction following
TL;DR: We introduce a new method for collecting complex demonstrations by interacting with a browser, and propose a language based pruning heuristic to effectively navigate the exponential number of possible interactions on a website
Abstract: We introduce NNetscape Navigator (NNetnav), a method for training web agents entirely through synthetic demonstrations. These demonstrations are collected by first interacting with a browser to generate trajectory rollouts, which are then retroactively labeled into instructions using a language model. Most work on training browser agents has relied on expensive human supervision, and the limited previous work on such \emph{interaction-first} synthetic data techniques has failed to provide effective search through the exponential space of exploration. In contrast, NNetnav exploits the hierarchical structure of language instructions to make this search more tractable: complex instructions are typically decomposable into simpler subtasks, allowing NNetnav to automatically prune interaction episodes when an intermediate trajectory cannot be annotated with a meaningful sub-task. We use NNetnav demonstrations from a language model for supervised fine-tuning of a smaller language model policy, and find improvements of 6 points on WebArena and over 20 points on MiniWoB++, two popular environments for web-agents. Notably, on WebArena, we observe that language model policies can be further enhanced when fine-tuned with NNetnav demonstrations derived from the \emph{same} language model. Finally, we collect and release a dataset of over 6k NNetnav demonstrations on WebArena, spanning a diverse and complex set of instructions.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 12712
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