BR-MoE, a memory-budget-aware co-design framework that treats per-expert bit-width and compensator rank as joint variables under a single memory constraint, establishes a superior memory–accuracy trade-off over state-of-the-art quantization methods, delivers substantial end-to-end inference speedups, and re-derives an...
Lei Jiang, Jing-Wei Sun, Jun-Qing Lin et al.· Proceedings of the Internati...· 0 citations
The Mixture-of-Experts (MoE) architecture has become the dominant paradigm for scaling Large Language Models, yet its explosive parameter growth routinely overflows the GPU memory budget. Weight quantization offers a practical way to fit these models into limited GPU memory, but reducing precision below 4 bits causes s...
Lei Jiang, Jingwei Sun, Jun-Qing Lin et al.· Proceedings of the Internati...· 0 citations
Adaptive FastOPD is proposed, a progress-aware strategy that expands the rollout horizon only when learning near the current boundary region has plateaued and the current horizon is sufficiently utilized, and remains robust across a range of hyperparameter settings.
The results suggest that coupling generation with executable verification and feedback-guided refinement is an effective way to improve text-to-molecule generation.
Qian Tan, Xuanyu Zhu, Lei Jiang et al.· arXiv.org· 0 citations
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