25 citations · 34 across the 3 of their papers we have counts for
3 papers
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Avi Singh, John D. Co-Reyes, Rishabh Agarwal +38
Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of hi…
Guided evolutionary strategies: Augmenting random search with surrogate gradients
Niru Maheswaranathan, Luke Metz, George Tucker +2
Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with…
Stochastic natural gradient descent draws posterior samples in function space
Samuel L. Smith, Daniel Duckworth, Semon Rezchikov +2
Recent work has argued that stochastic gradient descent can approximate the Bayesian uncertainty in model parameters near local minima. In this work we develop a similar correspond…