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cs.LG2026
JumpStart Your Policy Learning with Lessons from 160,000 Training Runs
Nabil Omi, Eric Bae, Chung Yik Edward Yeung +2
Reliable progress in offline policy learning depends on careful reporting, well-tuned baselines, and evaluation across diverse conditions. Prior work has shown that results can be…
cs.LG2025
Load Balancing Mixture of Experts with Similarity Preserving Routers
Nabil Omi, Siddhartha Sen, Ali Farhadi
Sparse Mixture of Experts (MoE) models offer a scalable and efficient architecture for training large neural networks by activating only a subset of parameters ("experts") for each…