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10月13日
ModelsBuildersSelected03:56
Meta tests Muse Wonder Full scaling on unfamiliar tasks

The finding suggests base model quality, not more post-training, drives generalisation to tasks outside the training distribution — a useful signal for teams deciding where to spend compute. Researchers evaluating reasoning models can use this framing to compare candidates on out-of-distribution scaling curves rather than benchmark scores alone. No regional angle exists; the most concrete comparison is Muse Wonder Full against the Muse Spark 1.3 trend line.

官方账号Meta AI@AIatMetaSource