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Selecting relevant evidence spans for test-time training can boost long-context LLM accuracy by up to 15%.
Forget resource-intensive pipelines: a purely academic team achieves SOTA search agent performance with just 10.6k SFT data points, outperforming models trained with CPT+SFT+RL.
