Adaptive-WAM: Quality-Guided Early-Exit Planning from Intermediate Video-Diffusion Features

2026-08-06 · 13:13 UTC ·Sining Ang, Yuguang Yang, Yan Wang ·1 min read

Brief

Adaptive-WAM introduces a quality-guided, multi-exit planner that decodes reliable ego trajectories from intermediate Diffusion Transformer (DiT) layers of a Wan2.2-5B video-diffusion backbone, using trajectory-diffusion heads plus a lightweight scorer to terminate inference early. The authors show planning is insensitive to video-noise timesteps, report 90.8 PDMS (single) and 92.6 PDMS (fixed, 64 proposals) on NAVSIM, 89.9 EPDMS on NAVSIM v2, strong zero-shot transfer to nuScenes (0.88 m L2, 0.08% collisions), and lower A100 latency (170 ms) versus full-depth baselines.

Why it matters

Adaptive-WAM is a quality-aware multi-exit world-action planner built on a Wan2.2-5B backbone that attaches trajectory-diffusion heads to selected Diffusion Transformer (DiT) blocks and uses a lightweight trajectory-quality scorer to early-exit, avoiding the iterative classifier-free denoising loop and VAE decoding required for full future-video synthesis.

Key details

On NAVSIM, the adaptive single-trajectory planner achieves 90.8 PDMS; a fixed-exit variant reaches 92.6 PDMS with 64 proposals. On NAVSIM v2 it attains 89.9 EPDMS. Without target-domain fine-tuning, Adaptive-WAM transfers to nuScenes with 0.88 m average L2 error and a 0.08% collision rate.

On an A100 GPU, adaptive routing increases PDMS from 90.62 to 90.79 while averaging 170 ms end-to-end planning latency — ~10% faster than a 190 ms fixed block-15 planner and ~47% faster than a 320 ms fixed full-depth planner.

Abstract

Large video diffusion models provide rich spatiotemporal priors for autonomous driving, but existing world-action models often inherit the cost of iterative future-video generation even though deployment only requires an ego trajectory. We ask a more basic question: how much of a video diffusion model must be executed to make a reliable driving decision? Through a controlled study of video denoising timesteps and Diffusion Transformer (DiT) depth, we find that planning performance is largely insensitive to the tested video-noise levels, whereas strong trajectories can already be decoded from intermediate layers. Based on this observation, we introduce Adaptive-WAM, a quality-aware multi-exit planner built on a Wan2.2-5B backbone. Trajectory diffusion heads are attached to selected DiT blocks, and a lightweight trajectory-quality scorer terminates inference once the best trajectory decoded so far satisfies a quality threshold; otherwise, computation continues from the cached hidden state to a deeper exit. The deployed planner therefore avoids the iterative classifier-free denoising loop and VAE decoding required for future-video synthesis, while dynamically allocating backbone depth according to trajectory quality. On NAVSIM, the adaptive single-trajectory planner achieves 90.8 PDMS; a separate fixed-exit variant reaches 92.6 PDMS with 64 proposals. It further obtains 89.9 EPDMS on NAVSIM v2, yielding the best reported results among the compared front-view video world-model planners. Without target-domain fine-tuning, Adaptive-WAM transfers to nuScenes with 0.88 m average L2 error and a 0.08\% collision rate. On an A100, adaptive routing improves PDMS from 90.62 to 90.79 while averaging 170 ms end-to-end planning latency, approximately 10\% below the 190 ms fixed block-15 planner and 47\% below the 320 ms fixed full-depth planner. Code will be released.