TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models

arXiv:2605.24292v1 Announce Type: new Abstract: Log-likelihood is a standard metric for evaluating generative models. Unfortunately, in contrast to autoregressive models (ARMs), discrete diffusion models generally do not admit exact computation of this quantity. Existing evaluations, therefore, rely on the evidence lower bound (ELBO), leaving unclear how much higher the true value may be. We address this by introducing the Tangent Upper Bound on Evidence (TUBE), a variational upper bound on log-likelihood that admits an unbiased Monte Carlo estimator. Our TUBE extends across latent-variable mo

Source: arXiv cs.LG — read the full report at the original publisher.

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