Generative recommendation has shown considerable potential in industrial recommendation systems. However, the performance of existing generative methods is limited by several challenges. 1) Semantic IDs (SIDs) are treated as one-dimensional sequences, overlooking the hierarchical dependencies among different layers. 2) Next-token prediction used is unable to explicitly optimize session-wise recommendation. 3) Although reinforcement learning has been applied to align generated recommendations with user preferences, few existing methods explicitly optimize the ranking of positive items in session-wise recommendation. To address the above issues, we propose RedMall, a generative recommender designed for large-scale e-commerce. Firstly, RedMall constructs hierarchical SIDs through residual quantization over multimodal representations. Secondly, RedMall introduces a lightweight SID encoder to explicitly model layer-wise dependencies in SIDs. Moreover, RedMall incorporates sparse mixture-of-experts, dual gated attention, and multi-target prediction to scale model size, enhance user interest representation, and optimize session-wise recommendation. We further design an exposure-aware loss that treats exposed but unclicked items as negative feedback. To better align user preferences and list-wise ranking objectives, we combine Group Relative Policy Optimization with an auxiliary LambdaRank loss, using both real user feedback and item ranking to guide optimization. Experiments on four public datasets-Amazon Beauty, Amazon Toys, Amazon Food, and MovieLens-1M---show that RedMall outperforms the evaluated baselines across all reported Recall and NDCG metrics. Online A/B tests show improvements of 2.24% in advertiser value (ADVV) and 0.59% in click-through rate (CTR) for e-commerce advertising, as well as gains of 1.08% in GMV and 0.95% in daily active buyers (DAB) for e-commerce recommendations.

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