Thoughts on AI alignment, machine learning research, and the evolving landscape of synthetic data.
Examining why RLHF faces fundamental limitations across scalability, human judgment quality, reward models, and governance that constrain the development of more capable AI …
The verdict is in. We deliver a scorecard on synthetic alignment, assessing which of RLHF's limitations have been solved and which remain, backed by six key empirical insights.
Exploring five critical research frontiers: meta-alignment, post-deployment adaptation, breaking the iteration ceiling, judge bias auditing, and co-evolution dynamics.
A quantitative case study comparing the costs of human preference labeling (RLHF) versus synthetic preference generation (RLAIF), demonstrating how computational approaches …
Mapping the design space of synthetic alignment methods—two foundational paradigms and eight critical design factors that shape implementation choices and trade-offs.
This four-part research series examines why RLHF faces fundamental limitations and how synthetic alignment methods are reshaping the field, distilling insights from 20+ recent …