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cs.LG2026
Efficient Pre-Training of LLMs through Truncated SVD Representations
Kaivan Kamali, Kajetan Schweighofer, Hormoz Shahrzad +3
LLM pretraining is extremely costly; therefore, parameter-efficient LLM architectures have recently emerged as a compelling research direction. One such promising approach is to re…
cs.LG2025
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
Xin Qiu, Yulu Gan, Conor F. Hayes +6
Fine-tuning large language models (LLMs) for downstream tasks is an essential stage of modern AI deployment. Reinforcement learning (RL) has emerged as the dominant fine-tuning par…