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This turned out to matter beyond just throughput. Rankings didn’t always transfer across hardware. For example, FINAL_LR_FRAC=0.03 sometimes beat 0.05 on H100 but consistently lost on H200. The likely explanation: with more training steps, the model benefits from keeping the learning rate higher toward the end of the schedule. The agent’s self-invented validation tier caught these discrepancies - a workflow a human researcher might design deliberately, but that the agent arrived at just by observing its own results.
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