[Paper] In-Place Test-Time Training

Summary

Current Large Language Models are limited by their static "train then deploy" paradigm, hindering dynamic adaptation to new information. Test-Time Training (TTT) offers a promising alternative by updating model parameters during inference, but faces significant challenges like architectural incompatibility and computational inefficiency in existing LLM ecosystems. This paper, "In-Place Test-Time Training," aims to address these critical barriers, potentially enabling more adaptive and continuously learning LLMs.

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