Why RLHF Can't Scale: Understanding the Fundamental Limitations
Examining why RLHF faces fundamental limitations across scalability, human judgment quality, reward models, and governance that constrain the development of more capable AI …
What Works in Synthetic Alignment: Evidence and Scorecard
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.
The Path Forward: Five Critical Research Frontiers
Exploring five critical research frontiers: meta-alignment, post-deployment adaptation, breaking the iteration ceiling, judge bias auditing, and co-evolution dynamics.
The Economics of Alignment: Why RLAIF Delivers 11x Cost Reduction
A quantitative case study comparing the costs of human preference labeling (RLHF) versus synthetic preference generation (RLAIF), demonstrating how computational approaches …
The Architecture of Synthetic Alignment: Paradigms and Design Factors
Mapping the design space of synthetic alignment methods—two foundational paradigms and eight critical design factors that shape implementation choices and trade-offs.
Synthetic Alignment Research: Key Insights for AI Leaders
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 …