Re-ranking refines the upstream ranked list to maximize listwise user utility. Existing two-stage generator-evaluator methods decouple list generation from list evaluation, leaving the generator without direct access to the listwise utility signal, while scoring-based one-stage methods score items under a fixed upstream permutation context without explicitly searching the permutation space. We propose \textbf{ReAlign}, a reward-guided preference alignment framework that couples a listwise reward model with a candidate-aware autoregressive generator. The reward model decomposes the listwise reward into position-wise contributions, modeling user exposure through a survival-based exposure module and item value through per-objective heads for heterogeneous engagement signals. The generator is trained in two stages: supervised pre-training on logged exposure lists to establish a relevance prior, followed by reward-guided post-training that aligns the generator with the reward model through complementary distribution and ranking alignment over within-pool standardized rewards. Extensive offline experiments show that ReAlign consistently outperforms existing state-of-the-art baselines.
Re-ranking is a crucial stage in recommender systems, yet existing evaluators oversimplify user browsing assumptions and collapse multi-objective signals into a single scalar, limiting their ability to reflect true listwise utility. We propose \textbf{SurvEval}, a listwise evaluator that jointly models user exposure and item value for re-ranking evaluation. We formalize the sequential browsing process as a discrete-time survival model, deriving monotonically decreasing reaching probabilities that capture the cascading structure of user browsing. We further introduce a multi-objective value estimation module with dedicated per-objective prediction heads and uncertainty-based loss weighting to adaptively balance objectives of varying scales and noise levels. Both components are jointly optimized via a unified transformer backbone, and the listwise utility is obtained by summing the position-wise products of reaching probabilities and conditional item value estimates. Extensive experiments on KuaiRand and Avito demonstrate that SurvEval achieves superior performance over state-of-the-art baselines.