PFP-World: Pre-Policy Planning for Web Agents with a Page Functional Primitive-based World Model

Large language model-based web agents struggle with long-horizon cross-page web navigation, as they cannot proactively anticipate environmental dynamics. World models could resolve this by simulating page state transitions in advance, yet existing web world models mainly serve post-hoc purposes: they either generate full trajectories for policy training or evaluate atomic actions derived from flawed subgoal reasoning, lacking a mechanism to screen candidate page-level goals prior to execution. To fill this gap, we propose PFP-World, a pre-policy world-model planner built on Page Functional Primitives (PFPs). PFPs denote page-level functional intentions, serving as unified semantic abstractions that group atomic actions by shared functional intent (e.g., search, checkout). We encode each page state as an inventory of available PFPs. PFP-World redefines three core components of conventional web world models: it shifts the world model’s role from a post-hoc trajectory generator or policy evaluator to a pre-policy planner, replaces raw HTML DOM state encoding with abstract PFP inventories, and uses semantic PFPs as high-level control signals for state transition instead of atomic action spaces. With our PFP-World, the planned primitives guide downstream action grounding by mapping each high-level functional intent to executable web operations. We further introduce a page-centric bottom-up paradigm to learn site dynamics from PFP transitions without relying on complete task trajectories. Experiments on the standard WebArena benchmark and a self-constructed cross-page benchmark (namely CrossPageBench) show that PFP-World-SFT outperforms existing world-model-based baselines, bringing a 20.7 percentage-point gain in overall success rate over the reproduced WMA baseline on WebArena and gains of 19.8 and 12.8 percentage points in average target completion over the reproduced RLVR-World baseline on the Shopping and GitLab sites of CrossPageBench, respectively.