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iDriveVLA is proposed, a multi-modal planning framework that improves the candidate trajectory space while enabling more reliable and context-aware trajectory evaluation and develops an oracle-aligned progressive training strategy consisting of candidate imitation pretraining, candidate space refinement, and semantic r...
DiffAdapterVLA is introduced, which realizes Planning in the Backbone: it injects explicit trajectory tokens into selected VLM late layers, bringing trajectory state into backbone forward computation, where it co-evolves with driving conditions at different depths.
A novel framework that aligns multi-trajectory supervision with policy optimization, and introduces two complementary mechanisms: feasibility-first advantage assignment and dynamic distillation to ensure that expanded trajectory supervision is effectively absorbed during policy optimization.
SimWAM, a simple yet effective WAM that leverages future-video prediction as a training-time supervision signal, co-trains a pretrained video expert and a lightweight action expert with joint flow matching and applies reinforcement learning to optimize a compositional driving reward beyond trajectory imitation.
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