Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning
arXiv:2608.14706v1 Announce Type: new Abstract: Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedules, limiting inference procedures from adapting to the data. We introduce Equilibrium Forcing (EqF), a simplified framework for video denoising generative models without noise level conditioning. EqF pioneers modular training- and inference-time designs for noise-unconditional generation that decouple learning the denoising field from sampling. This flexibility allows for inference-time algorithms that operate in a closed loop by adapting to feedback from the sample, improving video quality and consistency on challenging autoregressive video generation benchmarks. Extensive analysis elucidates exactly how removing the noise level conditioning enables EqF's data-dependent inference properties to surpass the performance of standard noise level-conditional denoising video methods.
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[Submitted on 11 Aug 2026]
Title:Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning
View a PDF of the paper titled Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning, by Hansen Jin Lillemark and Alex Rojas and Zachary Novack and Runqian Wang and Yilun Du and Yian Ma and Taylor Berg-Kirkpatrick and Rose Yu
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Abstract:Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedules, limiting inference procedures from adapting to the data. We introduce Equilibrium Forcing (EqF), a simplified framework for video denoising generative models without noise level conditioning. EqF pioneers modular training- and inference-time designs for noise-unconditional generation that decouple learning the denoising field from sampling. This flexibility allows for inference-time algorithms that operate in a closed loop by adapting to feedback from the sample, improving video quality and consistency on challenging autoregressive video generation benchmarks. Extensive analysis elucidates exactly how removing the noise level conditioning enables EqF's data-dependent inference properties to surpass the performance of standard noise level-conditional denoising video methods.
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Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2608.14706 [cs.CV]
(or arXiv:2608.14706v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.14706
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hansen Lillemark [view email] [v1] Tue, 11 Aug 2026 00:02:32 UTC (9,033 KB)
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