Toby Ord argues reinforcement-learning scaling is approaching an effective limit

Toby Ord analyzed public o1, o3, and GPT-5 performance curves and argued that reinforcement-learning scaling requires orders of magnitude more compute for continued gains and may be nearing an effective limit. He presented this as a quantitative constraint with important uncertainties, not as proof that frontier progress has stopped.