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Skills stabilize agent execution by transforming noisy trajectories into procedural anchors, but they can fail under brittle assumptions and incompatible contexts.
LLMs align even better with human preferences when trained on *less* data, revealing that preference signals are surprisingly concentrated in the initial tokens of responses.