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PPO-Clip's stationarity and critic tracking are jointly bounded by a common amplification factor dependent on algorithmic parameters, revealing a unified error mechanism.
LLMs can now learn to reliably optimize tensor programs step-by-step, thanks to a new dataset that provides grounded, verifiable supervision at each transformation.