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  "text": "LIFT is a new language model design that lets a Transformer send more than one token's worth of information from its deepest layer back to its first layer at every step.\n\nPer the creator, the model feeds a latent state from its last layer back into its first layer and still pretrains fully in parallel. The video explains the bottleneck it targets and shows validation loss curves where, at 1B parameters, a standard Transformer needs about twice the data to catch up.\n\nWhy it matters: language models mostly improve by adding more data and compute, so an architecture that learns more from the same data could make training far more efficient.\n\nCredit: @TiroshDor https://t.co/tvj5mECkRD",
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