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.