Unverified50% confidenceFactExact time
Traditional sOED requires re-solving an optimization problem for the current posterior at each measurement, with computational cost accumulating over measurement steps; the RL method instead shifts the computational burden to the offline training stage, with only policy inference at the online stage.
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11/3/2026
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UnverifiedA common practice in observation normalization is to use a running mean/std to normalize observations online, but care must be taken to freeze the statistics to the inference side after training ends, otherwise distribution shift will occur during deployment.76% similarUnverifiedEvery step of RL training requires complete inference sampling, reward computation, and backpropagation, with rollout being the most time-consuming part.74% similarUnverifiedPost-hoc calculations show that, under all trained need growth rates, trajectories that actually solve the task achieve higher returns on the sampled layouts than the upper bound on returns reached by reset-based score farming.73% similarUnverifiedParameter count is not the only factor determining performance; training data quality, training strategies (such as instruction fine-tuning and RLHF), and the context length at inference time are equally critical.72% similarUnverifiedThe Waypoint-OPD method introduces intermediate checkpoints of RL training as waypoints, separately computing the relative action preference changes in the early stage (initial→intermediate) and the late stage (intermediate→endpoint), in order to determine the consistency of the direction of policy shift.72% similar
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