MLE-World released: reusing experience to cut experiment feedback cost, complementing RPG
sherryyangML · x · 2026-10-23
sherryyangML's team releases MLE-World, led by @kobrian23869, complementing their earlier Reward-Rate Policy Gradient (RPG) work on MLE agents: RPG learns to maximize reward per unit time, while MLE-World reuses past experience to make experiment feedback cheaper — two complementary paths to better AI research agents under a limited budget.
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