Sequential recommendation requires capturing diverse user behaviors, which a single network often fails to capture. While ensemble methods mitigate this by leveraging multiple networks, training them all from scratch leads to high computational cost and instability from noisy mutual supervision. We propose {\bf F}rozen and {\bf L}earnable networks with {\bf A}ligned {\bf M}odular {\bf E}nsemble ({\bf 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 {\it modular ensemble}. By decomposing each network into sub-modules (e.g., layers or blocks) and dynamically combining them, FLAME generates a rich space of diverse representation patterns. To stabilize this process, we pretrain and freeze one network to serve as a semantic anchor and employ {\it guided mutual learning}. This aligns the diverse representations into the space of the remaining learnable network, ensuring robust optimization. Consequently, 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 7.69$\times$ faster convergence and 9.70\% improvement in NDCG@20. We provide the source code of FLAME at https://github.com/woo-joo/FLAME_SIGIR26.

翻译：序列推荐需要捕获多样化的用户行为，单一网络往往难以实现。集成方法通过利用多个网络缓解此问题，但从头训练所有网络会导致高昂的计算成本以及噪声互监督带来的不稳定性。我们提出冻结网络与可学习网络的模块化对齐集成（FLAME）——一种新颖框架，将集成级别的多样性浓缩至单一网络，实现高效序列推荐。训练过程中，FLAME通过模块化集成仅用两个网络模拟指数级多样性：将每个网络分解为子模块（如层或块）并动态组合，生成丰富的多样化表示模式空间。为稳定该过程，我们预训练并冻结一个网络作为语义锚点，并采用引导式互学习，将多样化表示对齐至剩余可学习网络的空间中，确保鲁棒优化。因此，推理时FLAME仅使用可学习网络，即可实现与集成方法相当的性能，且零额外开销（相比单一网络）。在六个数据集上的实验表明，FLAME优于最先进基线，收敛速度最高提升7.69倍，NDCG@20提升9.70%。我们在https://github.com/woo-joo/FLAME_SIGIR26 提供FLAME源代码。