TokenMixer-Large: Scaling Up Large Ranking Models in Industrial Recommenders

In recent years, the study of scaling laws for large recommendation models has gradually gained attention. Works such as Wukong, HiFormer, and DHEN have attempted to increase the complexity of interaction structures in ranking models and validate scaling laws between performance and parameters/FLOPs by stacking multiple layers. However, their experimental scale remains relatively limited. Our previous work introduced the TokenMixer architecture, an efficient variant of the standard Transformer where the self-attention mechanism is replaced by a simple reshape operation, and the feed-forward network is adapted to a pertoken FFN. The effectiveness of this architecture was demonstrated in the ranking stage by the model presented in the RankMixer paper. However, this foundational TokenMixer architecture itself has several design limitations. In this paper, we propose TokenMixer-Large, which systematically addresses these core issues: sub-optimal residual design, insufficient gradient updates in deep models, incomplete MoE sparsification, and limited exploration of scalability. By leveraging a mixing-and-reverting operation, inter-layer residuals, the auxiliary loss and a novel Sparse-Pertoken MoE architecture, TokenMixer-Large successfully scales its parameters to 7-billion and 15-billion on online traffic and offline experiments, respectively. Currently deployed in multiple scenarios at ByteDance, TokenMixer -Large has achieved significant offline and online performance gains.

LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders

Modeling ultra-long user behavior sequences is critical for capturing both long- and short-term preferences in industrial recommender systems. Existing solutions typically rely on two-stage retrieval or indirect modeling paradigms, incuring upstream-downstream inconsistency and computational inefficiency. In this paper, we present LONGER, a Long-sequence Optimized traNsformer for GPU-Efficient Recommenders. LONGER incorporates (i) a global token mechanism for stabilizing attention over long contexts, (ii) a token merge module with lightweight InnerTransformers and hybrid attention strategy to reduce quadratic complexity, and (iii) a series of engineering optimizations, including training with mixed-precision and activation recomputation, KV cache serving, and the fully synchronous model training and serving framework for unified GPU-based dense and sparse parameter updates. LONGER consistently outperforms strong baselines in both offline metrics and online A/B testing in both advertising and e-commerce services at ByteDance, validating its consistent effectiveness and industrial-level scaling laws. Currently, LONGER has been fully deployed at more than 10 influential scenarios at ByteDance, serving billion users.