Awakening dormant users, who remain engaged but exhibit low conversion, is a key driver for incremental GMV growth in e-commerce platforms. Existing methods often underperform since they typically rely on single-step estimation of an item’s intrinsic value (e.g., immediate click probability). This overlooks the instrumental effect of items, where specific interactions act as triggers to shape latent intent and drive subsequent decisions along a conversion trajectory. To bridge this gap, we propose RoleGen, a framework that synergizes a Conversion Trajectory Reasoner with a Generative Collaborative Executor. Specifically, the LLM-based Reasoner explicitly models the context-dependent Functional Role of items to reconstruct intent evolution. It further uses counterfactual role exploration to mitigate interest collapse and generate diverse conversion-oriented candidates. These reasoned candidate items are then injected into the Executor, which is optimized via a feedback-driven co-training mechanism to ensure grounded execution. Extensive offline experiments and online A/B testing on a large-scale e-commerce platform show a 5.82% gain in Recall@1 and a 6.71% increase in online order volume, confirming RoleGen’s effectiveness in activating dormant users.

As large language models continue to scale, computational costs and resource consumption have emerged as significant challenges. While existing sparsification methods like pruning reduce computational overhead, they risk losing model knowledge through parameter removal. This paper proposes DSMoE (Dynamic Sparse Mixture-of-Experts), a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge based on input complexity. Additionally, we introduce a sparsity loss term to balance performance and computational efficiency. Extensive experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches across language modeling and downstream tasks, particularly excelling in generation tasks. Analysis reveals that DSMoE learns distinctive layerwise activation patterns, providing new insights for future MoE architecture design.