Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients

• URL: http://arxiv.org/abs/2606.07400v1
• Date: Fri, 05 Jun 2026 15:41:25 GMT
• Title: Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients
• Abstract summary: Deep learning approaches such as variational autoencoders typically form artificial latent states rather than the mechanistic ground-truth states.<n>We introduce GReinSS, a principled policy framework that uses dynamically rescaled graphs to learn latent state distributions.<n> GReinSS is practically effective for generative modeling and inference of latent states from indirect observations.
• Score: 7.16278619735062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many scientific problems require inferring unobserved mechanistic latent states from indirect observations. While classical approaches, including expectation maximization, do not scale to combinatorially large spaces, deep learning approaches such as variational autoencoders typically form artificial latent states rather than reconstructing the mechanistic ground-truth states. Here, we introduce GReinSS, a policy learning framework that uses dynamically rescaled rewards to learn latent state distributions that maximize the observed data likelihood. We show that GReinSS accurately reconstructs simulated latent sets and latent graphs, outperforming alternative policy learning and generative modeling baselines. Additionally, GReinSS reconstructs isoforms from real short-read RNA sequencing data that better match isoforms detected by orthogonal long-read sequencing than the standard RSEM algorithm. Overall, GReinSS is a principled and practically effective approach for generative modeling and inference of combinatorial latent states from indirect observations.

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