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The landscape of deep learning optimizers is vast, but this paper cuts through the noise to reveal the fundamental trade-offs and promising future directions for efficient, robust, and trustworthy training.
A principled framework for General World Models reveals the limitations of current systems and the architectural requirements for future progress.
