FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation

Sequential recommendation requires capturing diverse user behaviors, which a single network often fails to capture. While ensemble methods mitigate this, training multiple networks from scratch incurs high computational cost and instability from noisy mutual supervision. We propose Frozen and Learnable networks with Aligned Modular Ensemble (FLAME), a novel framework that condenses ensemble-level diversity into a single network for efficient sequential recommendation. During training, FLAME simulates exponential diversity using only two networks via modular ensemble, which dynamically combines sub-modules (e.g., layers) of each network to generate a rich space of diverse representation patterns. To stabilize training, FLAME pretrains and freezes one network as a semantic anchor and employs guided mutual learning to align diverse representations into the space of remaining learnable network. At inference, FLAME utilizes only the learnable network, achieving ensemble-level performance with zero overhead compared to a single network. Experiments on six datasets show that FLAME outperforms state-of-the-art baselines, achieving up to 7.69x faster convergence and 9.70% improvement in NDCG@20. Our code is available at https://github.com/woo-joo/FLAME_SIGIR26.