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Looped flows train recurrent updates with local denoising
Published on September 12, 2026, the article proposes training recurrent hidden-state updates with local denoising objectives, seeking to address a difficulty associated with backpropagation across only a few updates.
On September 12, 2026, an article proposed looped flows: an approach for training recurrent hidden-state updates with local denoising objectives. The proposal seeks to address the difficulty of training early updates to support later ones when backpropagation covers only a few updates.
The text notes that engineers exploring recurrent inference architectures may consider this alternative training objective. To verify the proposal’s scope and findings, consult the original article and check its methods and supporting evidence; the available summary gives no quantitative results.
Original source: Looped Flows Treinam Atualizações Recorrentes com Denoising Local