cMoLLM at Scale: Horizontal Scaling Laws for Mixture-of-LLMs
Scaling large language models (LLMs) has driven their success, yet dense Transformers couple capacity and computation: every parameter is activated for every token, making training and inference costs grow linearly with model size-a critical bottleneck as models approach trillion-parameter regimes. We aim to scale capacity through MoE-style mixture throughout the LLM pipeline rather than only the FFN. Prior pipeline-level approaches include ParaScale, which introduces virtual tokens and parallel streams but incurs substantial overhead and suffers from homogenized routing and gradient collapse, and AltUp, which uses an auxiliary prediction branch but offers limited adaptivity and slow conv...
| Authors | Xin Yang, Yemin Wang, Mingda Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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