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Meta says Muse Wonder Full handles unfamiliar tasks better than Muse Spark 1.3

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Meta compared test-time compute scaling for two models on tasks seen during post-training and on unfamiliar tasks. Both models improve similarly on familiar tasks, but Muse Wonder Full scores higher out of distribution than the Muse Spark 1.3 trend predicts. Meta attributes this to a stronger base model that transfers post-training gains to unanticipated tasks.

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To quantify how the models handle unfamiliar tasks, we compared test-time compute scaling on tasks trained on during post-training against other tasks.

Both models improve similarly in-distribution, but given its out-of-distribution score, Muse Wonder Full performs better on unfamiliar tasks than the Muse Spark 1.3 trend predicts. We attribute this upward shift to a stronger base model, which carries post-training gains over more effectively to tasks we didn't anticipate.

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