Training large language models (LLMs) entails a fundamental trade-off: memory-efficient optimizers such as Adam discard cross-parameter curvature, whereas full-curvature methods such as SOAP can accelerate convergence at prohibitive memory costs. We introduce Clean, a memory-efficient and full-curvature optimizer desig...
Beheshteh T. Rakhshan, S. Rajabi, Maziar Sargordi Shikai Fang et al.· 0 citations
Results show the need for evaluations to account for the disjunction between what models represent internally, what they verbalize, and their steering, and the need for evaluations to account for the disjunction between what models represent internally, what they verbalize, and their steering.
FlowMeas is introduced, which uses a generative flow network to directly sample finite ensembles of shallow Clifford measurement circuits subject to a prescribed shot budget and hardware constraints, and establishes generative learning as a flexible and unified framework for quantum measurement design under practical r...
Jun Dai, O. Nahman-Lévesque, Guillaume Rabusseau et al.· 0 citations
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