A concrete path past the data wall: train agents on interaction trajectories, not just text
TuXinming · x · 2026-09-09
Responding to complaints that scaling talk is fortune-telling, TuXinming proposes a concrete loop: pretraining compresses world→observation→text, but tools offer act→feedback→train-on-trajectories. Labs already train on tool-use/agent traces — e.g., agents querying AlphaGenome to refine hypotheses; swap tools for real experiments and feedback comes from the world itself.
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