2 papers
cs.LG2026
Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning
Mingyu Cao, Gen Li, Jie Ji +6
Mixture-of-Experts (MoE) has garnered significant attention for its ability to scale up neural networks while utilizing the same or even fewer active parameters. However, MoE does…
cs.CL2025
TODO: Enhancing LLM Alignment with Ternary Preferences
Yuxiang Guo, Lu Yin, Bo Jiang +1
Aligning large language models (LLMs) with human intent is critical for enhancing their performance across a variety of tasks. Standard alignment techniques, such as Direct Prefere…