Summary
Fitting scaling laws, crucial for planning multi-million dollar AI training runs, is currently a very expensive process that can cost millions itself. This paper introduces a budget-aware sequential experimental design approach to address this, treating it as a problem of selecting the most informative pilot experiments from a pool with heterogeneous costs. The goal is to maximize efficiency and information gain while minimizing the overall expenditure on fitting these critical laws.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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