Princeton's Mengdi Wang: RL in static environments may only reinforce high-probability modes, leaving a generalization gap

MengdiWang10 · x · 2026-08-25

Princeton professor Mengdi Wang proposes a broader hypothesis: scaling models and RL in static environments may increasingly optimize the discovery and reinforcement of high-probability modes already present in the data, while leaving a fundamental generalization gap outside them.

She argues this matters enormously for scientific AI, since science's most valuable outcomes are often not high-probability trajectories already represented in the data. That's why she focuses on closing the loop between AI and real-world scientific verification: letting models experiment, encounter genuinely novel outcomes, and update their understanding of the physical world, rather than just learning better modes from existing data.

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Princeton's Mengdi Wang: RL in static environments may only reinforce high-probability modes, leaving a generalization gap

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