"Zhao and colleagues found that chain-of-thought performance was substantially more reliable on familiar distributions than on tasks involving unseen transformations, lengths, or formats."
The researchers examined generalization across task composition, sequence length, and input format in controlled experiments, supplemented by tests involving pretrained models. Their findings established pronounced sensitivity to distribution discrepancies, explaining how apparently successful reasoning procedures can become brittle when the conditions of evaluation change.
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