Afterobtaining an optimistic and and aligned critic, we perform constrained fine-tuning to combat distribution shift during online learning.

The Counterfactual Regret Minimization (CFR) algorithm Zinkevich et al. [2007] is crucial for solving extensive-form games with incomplete information.

We mend this problem by adding an embedding to each digit that encodes its position relative to the start of the number.

Learning from active human involvement enables the human subject to actively intervene and demonstrate to the AI agent during training. The interaction and corrective feedback from human brings safety and AI alignment to the learning process.

Our cost measures the work done by the simulation procedure to transport samples from one point in the diffusion path to the next. Our method does not require hyperparameter tuning and adapts to the dynamics and geometry of the diffusion path. Our algorithm only involves the evaluation of the estimated Stein score, making it scalable to existing pre-trained models at inference time and online during training.

We show how techniques from the theory of error-correcting codes can be applied to achieve scalability and hence improved performance. As an exemplar, we focus on how polar codes can be used to efficiently simulate i.i.d.