What is recorded here?
Conditioned one generative network on noise level and desired step size to support one-step and variable-budget sampling within a single training phase.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits flow matching, consistency learning and bootstrapping. One network/phase does not remove training computation or guarantee exact consistency for every learned solution; claimed quality advantages are original benchmark comparisons.
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People and systems
Kevin Frans; Danijar Hafner; Sergey Levine; Pieter Abbeel
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
Shortcut models: step-size-conditioned generative learning
Conditioned one generative network on noise level and desired step size to support one-step and variable-budget sampling within a single training phase.
Scientific scope
Inherits flow matching, consistency learning and bootstrapping. One network/phase does not remove training computation or guarantee exact consistency for every learned solution; claimed quality advantages are original benchmark comparisons.
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
Kevin Frans; Danijar Hafner; Sergey Levine; Pieter Abbeel
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.