AI Research
FARO: Function-Space Adaptive Return-to-Risk Optimization via Cross-Block Equal-Risk-Contribution Budget Allocation
FARO targets hand-designed optimizer splits in large language model training
Training a large language model is compute-bound, and current recipes divide the work between optimizers by hand: orthogonalized momentum for attention and MLP matrices, Adam-style updates for the rest.
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ArXiv CS.LG
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ArXiv CS.LG
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Generated insight by OnAirToday AI
Hand-tuned optimizer choices split LLM training across different parameter groups. FARO’s focus on adaptive, cross-block risk allocation points to a way of structuring that division systematically.
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