Discriminative World Models for Web Agents
[Sztuczna inteligencja] Autorzy: Kelvin Li, Dhruv Pendharkar, Anish Pahilajani i 6 więcej
Recent web agents use world models for test-time action selection by sampling candidate actions, predicting the resulting web states, and ranking them with a ranker model or a Process Reward Model (PRM). These world models are typically trained via supervised next-state prediction to generate fixed representations like HTML or AXTree snapshots. However, this objective is misaligned with the downstream ranker, which relies on predicted states being discriminative across candidates to accurately score them. To address this, we introduce predicted-state matching, a training objective where the predicted representation must distinguish the true resulting state from those reached by alternative actions. We train these models using a branching web-agent dataset derived from WebArena Go-Browse trajectories, where every decision point contains multiple alternative actions and their resulting st…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja