In the oracle continuous-time setting, stochastic interpolation models recover training samples exactly, with deviations controlled by discretization and estimation errors, leading to theoretical definitions of overfitting and underfitting.
Do generated data always help contrastive learning?arXiv preprint arXiv:2403.12448
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SimReg regularization accelerates LLM pretraining convergence by over 30% and raises average zero-shot performance by over 1% across benchmarks.
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A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models
In the oracle continuous-time setting, stochastic interpolation models recover training samples exactly, with deviations controlled by discretization and estimation errors, leading to theoretical definitions of overfitting and underfitting.
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SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization
SimReg regularization accelerates LLM pretraining convergence by over 30% and raises average zero-shot performance by over 1% across benchmarks.