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backpropagation-through-time

topic2 events

papersTODAY 04:00 UTC

Local learning rule trains generative thermodynamic computers

A new preprint describes training generative thermodynamic computers, which convert thermal noise into structured data via Langevin dynamics, using an update applied locally at each integration step. The authors derive the coupling gradient from a reverse-path Onsager-Machlup objective rather than relying on global backpropagation through time. The work appears on arXiv under the machine learning category as a cross-listed submission.

research/thermodynamic-computing/training/arxiv

papersSEP 12 04:00 UTC

arXiv paper examines looped models that update hidden states recurrently at inference

A new preprint discusses looped neural architectures that improve problem solving by repeatedly updating an internal hidden state during inference rather than scaling parameters. The authors note that training such models is challenging because backpropagation through repeated steps is costly. The work explores how these iteration-based approaches relate to spending more compute at test time.

research/arxiv/inference/recurrent-models