@omarsar0
RT @omarsar0: Banger paper from Stanford on efficient test-time scaling. If you run agents that think for a long time, this one is worth y…
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"text": "Banger paper from Stanford on efficient test-time scaling.\n\nIf you run agents that think for a long time, this one is worth your time.\n\n(bookmark it)\n\nLong reasoning keeps the entire trace in memory through full attention.\n\nThis means that the hardest problems, the ones that need the most thinking, are also the ones that cost the most to run.\n\nThe authors measured what the middle of a reasoning trace is actually worth.\n\nIntermediate tokens steadily lose importance as the model keeps going.\n\nTheir new approach, Prefix Sliding, drops those tokens. It keeps the prefix, which holds the instructions and the available tools, plus a window of the last few thousand tokens. Everything in between gets discarded during generation.\n\nTotal memory stays capped no matter how long the model reasons.\n\nWithout any training, this runs existing models 3x faster while matching full-attention performance, and it enables RL rollouts past 100,000 tokens.\n\nPaper: https://t.co/HzwSZ7fCdh\n\nChat with Paper: https://t.co/OfjtVjamIC",
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