New RLHF Paradigms
The recent advancements in RLHF highlight a shift towards a new standard recipe, emphasizing the importance of synthetic data and iterative training processes. As major labs like Apple, Meta, and Nvidia refine their approaches, the focus on data filtering emerges as a critical factor in achieving high-quality models. The evolving landscape suggests that human preference data, while valuable, presents challenges in transferability across different models.In this clip
From this podcast
Interconnects Audio
A recipe for frontier model post-training
Related Questions