What is recorded here?
Trained multimodal understanding and generation under a common next-group-of-tokens objective, with elastic depth, expert capacity and routing sparsity to support multiple deployment budgets from one run.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Distinct from ERNIE4.5's heterogeneous expert sharing. Elastic/supernetwork training and autoregression have prior art; no first-ever or universal optimality claim is imported. Submodel quality and post-training stability are source-reported.
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English catalogue narrative
ERNIE Team: elastic unified multimodal expert training
Trained multimodal understanding and generation under a common next-group-of-tokens objective, with elastic depth, expert capacity and routing sparsity to support multiple deployment budgets from one run.
Scientific scope
Distinct from ERNIE4.5's heterogeneous expert sharing. Elastic/supernetwork training and autoregression have prior art; no first-ever or universal optimality claim is imported. Submodel quality and post-training stability are source-reported.
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
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.