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cs.LG2026
Dynamic Expert Pruning for Multi-Agent Systems
Jabin Koo, Soheil Abbasloo, Sungjae Lee +1
Mixture-of-Experts (MoE) architectures scale language models efficiently by activating only a few experts per token, but the saving is confined to computation: every expert must st…
cs.LG2026
Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
Jabin Koo, Hoyoung Kim, Minwoo Jang +1
Federated Learning (FL) offers a privacy-preserving pathway for aligning Large Language Models (LLMs); however, existing frameworks typically enforce a monolithic reward model, ine…
cs.LG2024
Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients
Jabin Koo, Minwoo Jang, Jungseul Ok
Federated fine-tuning for Large Language Models (LLMs) faces significant challenges due to the heavy communication overhead of transmitting large model updates. Although Low Rank A…