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cs.LG2026
Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control
Ali Taghibakhshi, Ruisi Cai, Saurav Muralidharan +17
Training a family of large language models (LLMs), either from scratch or via iterative compression, is prohibitively expensive and inefficient, requiring separate training runs fo…
cs.LG2025
Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights
Jakub Krajewski, Marcin Chochowski, Daniel Korzekwa
Mixture of Experts (MoE) architectures have emerged as pivotal for scaling Large Language Models (LLMs) efficiently. Fine-grained MoE approaches - utilizing more numerous, smaller…