BAgger: Backwards Aggregation for Mitigating Drift in Autoregressive Video Diffusion Models

• URL: http://arxiv.org/abs/2512.12080v1
• Date: Fri, 12 Dec 2025 23:02:02 GMT
• Title: BAgger: Backwards Aggregation for Mitigating Drift in Autoregressive Video Diffusion Models
• Authors: Ryan Po, Eric Ryan Chan, Changan Chen, Gordon Wetzstein,
• Abstract summary: We introduce Backwards Aggregation (BAgger), a self-supervised scheme that constructs corrective trajectories from the model's own rollouts.<n>Unlike prior approaches that rely on few-step distillation and distribution-matching losses, BAgger trains with standard score or flow matching objectives.<n>We instantiate BAgger on causal diffusion transformers and evaluate on text-to-video, video extension, and multi-prompt generation.
• Score: 50.986189632485285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autoregressive video models are promising for world modeling via next-frame prediction, but they suffer from exposure bias: a mismatch between training on clean contexts and inference on self-generated frames, causing errors to compound and quality to drift over time. We introduce Backwards Aggregation (BAgger), a self-supervised scheme that constructs corrective trajectories from the model's own rollouts, teaching it to recover from its mistakes. Unlike prior approaches that rely on few-step distillation and distribution-matching losses, which can hurt quality and diversity, BAgger trains with standard score or flow matching objectives, avoiding large teachers and long-chain backpropagation through time. We instantiate BAgger on causal diffusion transformers and evaluate on text-to-video, video extension, and multi-prompt generation, observing more stable long-horizon motion and better visual consistency with reduced drift.

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