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I am worried about near-term non-LLM AI developments — LessWrong

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Almost all frontier models today share two major features in their training regime: they are trained offline and out of sequence. These features of training regimes make sense if you believe the classic function approximation or statistical approximation explanation of machine learning. In this story the model is meant to learn some fixed "target distribution" or "target function" by sampling i.i.d. data points from the training set. The model is then tested on a holdout "test set" which contains new input-output pairs from the same target distribution or function. If the model generalises across the train set and test set, it is considered a good model of the target. For many reasons, this story makes no sense when applied to the idea of AGI or trying to develop an ML model that is good at navigating the real world. Humans do not spend the first few years of their lives in a sensory deprivation tank, getting random webcam footage from different places on earth before they stop learnin

x I am worried about near-term non-LLM AI developments — LessWrong Crucial Considerations Forecasts (Specific Predictions) Reinforcement learning AI Frontpage 2025 Top Fifty: 14 % 260 I am worried about near-term non-LLM AI developments by testingthewaters 31st Jul 2025 6 min read 60 260 TL;DR I believe that: There exists a parallel track of AI research which has been largely ignored by the AI safety community. This agenda aims to implement human-like online learning in ML models, and it is now close to maturity. Keywords: Hierarchical Reasoning Model , Energy-based Model , Test time training

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