RL pioneer Richard Sutton calls synthetic data a "big mistake"

Turing Award winner Richard Sutton argues that scaling large language models on synthetic data is misguided because any simulation of an "infinitely complex" world is necessarily limited. He proposes agents that learn continually from real experience instead of relying on frozen, pretrained models.

Why it matters: Sutton co-authored the field's foundational reinforcement-learning textbook, so his critique adds weight to a growing split between labs scaling on synthetic data and researchers pushing continual/online learning as the next paradigm. It's a useful counterpoint given how central synthetic data has become to recent frontier model training.