Small 7B models can outperform much larger 32B models at web automation by learning high-level task decomposition through autonomous exploration and hindsight experience, rather than just memorizing low-level actions.
This paper improves small multimodal AI models for web automation by having them autonomously explore environments to learn task planning. The key innovation is using 'hindsight experience'—learning from failed attempts by reframing them as high-level tasks—which helps models generalize to new websites better than training on low-level atomic actions alone.