# SAGE (Sequence-level Adaptive Gradient Evolution)

The work identifies a "Symmetric Conservatism" failure mode of GBPO in generative recommendation (symmetric update bounds suppress rare positive signals such as cold-start items, static negative-sample constraints fail to prevent diversity collapse, and group-normalized multi-objective rewards yield low-resolution training signals) and proposes SAGE, which uses a geometric-mean sequence-level importance ratio and a decoupled multi-objective advantage estimator to reduce token-level variance and mitigate reward collapse, plus asymmetric adaptive bounding that applies a positive Boost to successful slates and an entropy-aware penalty to low-diversity failures; on three Amazon Product Reviews categories and the large-scale RecIF-Bench, the native-text TextRec optimized with SAGE outperforms O

The work identifies a "Symmetric Conservatism" failure mode of GBPO in generative recommendation (symmetric update bounds suppress rare positive signals such as cold-start items, static negative-sample constraints fail to prevent diversity collapse, and group-normalized multi-objective rewards yield low-resolution training signals) and proposes SAGE, which uses a geometric-mean sequence-level importance ratio and a decoupled multi-objective advantage estimator to reduce token-level variance and mitigate reward collapse, plus asymmetric adaptive bounding that applies a positive Boost to successful slates and an entropy-aware penalty to low-diversity failures; on three Amazon Product Reviews categories and the large-scale RecIF-Bench, the native-text TextRec optimized with SAGE outperforms O