With only black-box access to a recommendation API, injecting under 1% synthetic interactions makes a generative recommender promote an attacker's target far more often whenever a chosen trigger item appears, and short prefixes picked for decoding alignment raise the hit-rate lift by up to 16.3 percentage points.
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
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