PACE: POLICY-NATIVE ADAPTIVE DECISION TIMING FOR LONG-HORIZON REASONING

Long-horizon reasoning requires deciding not only what actions to take, but how many to execute open-loop before replanning. This number—the execution depth—balances replanning cost against compounding execution errors. Most current systems either fix execution depth as a hand-tuned scalar or adjust it at inference time using heuristic rules decoupled from the policy; both can be suboptimal. We treat execution depth as a learnable, history-conditioned variable of the policy itself and propose PACE, a model-native vision–language policy that jointly predicts what to execute and for how long under a hard decision budget. We evaluate PACE on four long-horizon environments spanning full and partial observability and visual and textual modalities: Sliding Puzzle, Sokoban, ALFWorld, and ScienceWorld. Across all seven evaluation settings, PACE improves success rates by 3.1–15.6 percentage points while using fewer decisions on average, achieving empirical success–decision Pareto dominance.