What is recorded here?
Performed autoregressive generated-context rollouts with KV caching during training and video-level losses, using few-step denoising and stochastic gradient truncation to reduce exposure mismatch.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits teacher/diffusion forcing, DMD and causal video generation. Training reduces an empirical distribution gap, not a proof of no error accumulation, indefinite stability or a physically accurate world model.
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People and systems
Xun Huang; Zhengqi Li; Guande He; Mingyuan Zhou; Eli Shechtman
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Self Forcing: generated-context rollout training for video diffusion
Performed autoregressive generated-context rollouts with KV caching during training and video-level losses, using few-step denoising and stochastic gradient truncation to reduce exposure mismatch.
Scientific scope
Inherits teacher/diffusion forcing, DMD and causal video generation. Training reduces an empirical distribution gap, not a proof of no error accumulation, indefinite stability or a physically accurate world model.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Xun Huang; Zhengqi Li; Guande He; Mingyuan Zhou; Eli Shechtman
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.