Not all data is created equal. Scaling quality control for data that can challenge PhDs and the most advanced LLMs demands a different approach. To meet this demand, we built something new: autoraters powered by multi-agent model debate. Here’s how it works 🧵
The demand for PhD-level, multimodal reasoning data is exploding. This type of data is critical for training models via reinforcement learning to tackle complex, extended reasoning. The challenges? Manual review is slow, even experts can miss subtle errors, and scaling the process is nearly impossible.
@scale_AI The future is hybrid: AI to accelerate, humans to align.
@scale_AI Data quality control is a game of PhDs and AI debates.
@scale_AI Love this autorater model - where's the human in the loop though? 😉