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Selecting relevant evidence spans for test-time training can boost long-context LLM accuracy by up to 15%.
Meta's new hierarchical indexing method lets you deploy massive recommendation models without sacrificing speed or accuracy, and it turns out the index itself highlights a high-quality subset of data perfect for test-time training.
