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cs.LG2024
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models
Michael Noukhovitch, Shengyi Huang, Sophie Xhonneux +3
The dominant paradigm for RLHF is online and on-policy RL: synchronously generating from the large language model (LLM) policy, labelling with a reward model, and learning using fe…
cs.LG2024★ 2 cited
The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization
Shengyi Huang, Michael Noukhovitch, Arian Hosseini +3
This work is the first to openly reproduce the Reinforcement Learning from Human Feedback (RLHF) scaling behaviors reported in OpenAI's seminal TL;DR summarization work. We create…