Generative Value Classifier

Computer Science > Machine Learning

[Submitted on 15 Oct 2026]

Title:Generative Value Classifier

View PDF HTML (experimental)Abstract:Q-learning is premised on learning a function to predict the value of a state-action pair, with the goal of using such a function to guide policy improvement. Most approaches learn this function from scratch using scalar or distributional return targets. This discriminative approach leaves another source of supervision for learning representations unused: the actions themselves. Rather than directly predict the return of a state-action pair, we ask the reverse question: how likely is the action under each possible return? This perspective provides a critic whose generative component is a return-conditioned policy, which can share the actor's architecture. Training this policy supervises the critic with every action rather than with returns alone, and a squared-error loss anchors the critic's Q-value to the bootstrapped target. We evaluate our approach on both offline and online RL tasks and show gains over leading discriminative methods, including distributional RL methods.

Submission history

From: Alexander Swerdlow [view email][v1] Thu, 15 Oct 2026 18:00:30 UTC (218 KB)

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