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cs.LG2026
SDG-MoE: Signed Debate Graph Mixture-of-Experts
Stepan Kulibaba, Kirill Labzin, Artem Dzhalilov +4
Sparse MoE models achieve a good balance between capacity and compute by routing each token to a small subset of experts. However, in most MoE architectures, once a token is routed…
cs.LG2025
AdLoCo: adaptive batching significantly improves communications efficiency and convergence for Large Language Models
Nikolay Kutuzov, Makar Baderko, Stepan Kulibaba +4
Scaling distributed training of Large Language Models (LLMs) requires not only algorithmic advances but also efficient utilization of heterogeneous hardware resources. While existi…