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It is shown that test-time communication can substantially outperform independent parallel attempts on challenging tasks, where sharing a breakthrough can push the whole group forward, and gains compound with scale, suggesting gains compound with scale.
A simple modification to supervised fine-tuning, TailSFT, which filters out already fit sequences during training, thereby focusing learning on under-modeled regions, or the tail, of the data distribution, and introduces a lightweight diagnostic for identifying settings where TailSFT is most likely to help.
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