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arXiv · 2509.24716

Discrete Variational Autoencoding via Policy Search

Michael Drolet·Firas Al-Hafez·Aditya Bhatt·Jan Peters·Oleg Arenz
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Abstract

Discrete latent bottlenecks in variational autoencoders (VAEs) offer high bit efficiency and can be modeled with autoregressive discrete distributions, enabling parameter-efficient multimodal search with transformers. However, discrete random variables do not allow for exact differentiable parameterization; therefore, discrete VAEs typically rely on approximations, such as Gumbel-Softmax reparameterization or straight-through gradient estimates, or employ high-variance gradient-free methods such as REINFORCE that have had limited success on high-dimensional tasks such as image reconstruction. Inspired by popular techniques in policy search, we propose a training framework for discrete VAEs that leverages the natural gradient of a non-parametric encoder to update the parametric encoder without requiring reparameterization. Our method, combined with automatic step size adaptation and a transformer-based encoder, scales to challenging datasets such as ImageNet and outperforms both approximate reparameterization methods and quantization-based discrete autoencoders in reconstructing high-dimensional data from compact latent spaces.

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Michael Drolet, Firas Al-Hafez, Aditya Bhatt, Jan Peters, Oleg Arenz. 2026-02-11. Discrete Variational Autoencoding via Policy Search. https://arxiv.org/abs/2509.24716

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