most citedWhat Matters In The Structured Pruning of Generative Language Models?

7 citations · 11 across the 3 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2024★ 2 cited

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Corby Rosset, Ching-An Cheng, Arindam Mitra +3

This paper studies post-training large language models (LLMs) using preference feedback from a powerful oracle to help a model iteratively improve over itself. The typical approach…

cs.LG2023★ 2 cited

Efficient RLHF: Reducing the Memory Usage of PPO

Michael Santacroce, Yadong Lu, Han Yu +2

Reinforcement Learning with Human Feedback (RLHF) has revolutionized language modeling by aligning models with human preferences. However, the RL stage, Proximal Policy Optimizatio…