RL-driven progress may hit a wall on out-of-distribution generalization, researcher argues
chris_j_paxton · x · 2026-09-04
- Chris Paxton argues the past year's AI progress explosion stems from RL, which directly conflicts with true out-of-distribution generalization on things we can't simulate well or simulate efficiently.
- Two paths forward: either world models get dramatically better, or humans keep an edge in domains where intuition and human learning speed matter.
- A sober technical take on the limits of the current RL/scaling paradigm, with implications for sim2real and robotics.
Related event: Researcher Argues RL Falls Short on Out-of-Distribution Generalization(2 posts)→
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