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Solving NP-hard combinatorial optimization problems like QAP just got a whole lot faster, thanks to a novel MCMC finetuning approach that achieves near-zero optimality gaps.
By strategically amplifying updates along flat directions in the loss landscape, LITE unlocks faster LLM pre-training with existing matrix-based optimizers like Muon and SOAP.
