Models Learn by Brute-Forcing Rollouts, Not by Understanding Like Humans
stuffyokodraws · x · 2026-08-18
The author highlights a striking contrast: humans learn by demonstration, reading and communication, compressing experience into abstractions—while models simply generate hundreds of rollouts and train on the successful ones. This brute-force selection of winners is weirdly effective, implying models never have to understand a domain the way we do.
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