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cs.LG2025
Improving LLM General Preference Alignment via Optimistic Online Mirror Descent
Yuheng Zhang, Dian Yu, Tao Ge +5
Reinforcement learning from human feedback (RLHF) has demonstrated remarkable effectiveness in aligning large language models (LLMs) with human preferences. Many existing alignment…
cs.LG2024
Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
Xu Ouyang, Tao Ge, Thomas Hartvigsen +3
We reveal that low-bit quantization favors undertrained large language models (LLMs) by observing that models with larger sizes or fewer training tokens experience less quantizatio…