Beyond One Path: Preserving Behavioral Diversity in Flow-Based End-to-End Driving

Flow-based policies offer a flexible foundation for multimodal trajectory generation in end-to-end driving. However, existing flow-based planning methods can produce nearly identical plans, limiting the useful alternatives available to the planner. We present a framework that addresses behavioral diversity across pretraining, sampling, and post-training. We first examine how flow pretraining shapes trajectory diversity, then construct a fixed noise vocabulary by optimizing coverage in decoded trajectory space across training scenes. We find that fixed noise seeds encode behavioral tendencies that persist across scenarios, while their decoded trajectories adapt to the current scene. Building on this structure, we propose mode-specialized post-training that locally refines each seed’s behavior on scenarios where it produces the highest-reward candidate, rather than optimizing every noise across all scenarios. Cross-scene group-relative advantages guide these updates, while behavior-retention regularization penalizes changes to other useful behaviors.