arXiv Machine Learning
Every Weight Is a Path of Shared Discrete Decisions
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Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling
arXiv:2606. 07404v1 Announce Type: new Abstract: This paper reports on training a hundred-billion-parameter sparse mixture of experts on a single eight-GPU node, end to end.
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Physics Guided Generative Optimization for Trotter Suzuki Decomposition
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arXiv Machine Learning
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Predicting Resource Efficient Hamiltonian Decomposition for Continuous-Time Quantum Walk Simulations
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arXiv Machine Learning
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Peak-Memory Schedules You Can Trust: Exact Realization, Certified Optimality, and the Limits of Recomputation for Inference
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