While training with static demonstrations has shown some promise, we show that such methods fall short for controlling real GUIs due to their failure to deal with real world stochasticity and non-stationarity not captured in static observational data.

Automating mathematical reasoning is a longstanding goal in artificial intelligence (Newell et al., 1957). A prominent line of work on the problem (Li et al., 2024) uses neural models to direct

In this work, we propose a novel calibration method that can be used to combat hallucinations.

In this paper, we study Adam in non-convex smooth scenarios with potential unbounded gradients and affine variance noise. We consider a general noise model which governs affine variance noise, bounded noise, and sub-Gaussian noise. We show that Adam with a specific hyper-parameter setup can find a stationary point with a O (1 / T) rate in high probability under this general noise model where T denotes total number iterations, matching the lower rate of stochastic first-order algorithms up to logarithm factors.

We introduce Annealed Multiple Choice Learning (aMCL) which combines simulated annealing with MCL.