Long-Term Optimization for Large-Scale Generative Retrieval with Off-Policy REINFORCE

Generative retrieval has become a popular paradigm for large-scale recommendation. However, it is typically trained with supervised next-item prediction objectives that do not directly optimize long-term user satisfaction. In this work, we formulate recommendation as a session-level sequential decision-making problem and introduce an autoregressive approach for training generative retrievers with off-policy REINFORCE on pre-collected data. Unlike the one-step off-policy correction used in prior work, we propose a multi-step approximation of importance weights enabled by the autoregressive formulation. To support offline evaluation, we train a user feedback model that simulates user responses to generated recommendations. This lets us adapt doubly robust off-policy evaluation for sequential decision-making to recommendation, a setting that has received limited attention. We further introduce a feedback-model-based test-time scaling procedure that simulates future responses and selects recommendations with the highest predicted long-term returns. Experiments on the public large-scale Yambda-5B dataset show that our RL agent improves offline estimates of cumulative session reward over next-item and next-positive prediction baselines, while largely preserving retrieval quality. Moreover, allocating more inference-time compute to simulating future responses improves model-based long-term return estimates without updating the policy.