Noisy labels are inevitable in large real-world datasets.

Our approach can be seamlessly integrated into existing video diffusion models.

In contrast, linear RNNs have low computational complexity and are suitable for long-range modeling.

The early theory of actor-critic methods considered convergence using linear function approximators for the policy and value functions. Recent work has established convergence using neural network approximators with a single hidden layer. In this work we are taking the natural next step and establish convergence using deep neural networks with an arbitrary number of hidden layers, thus closing a gap between theory and practice. We show that actor-critic updates projected on a ball around the initial condition will converge to a neighborhood where the average of the squared gradients is O (1 / m) + O (ϵ), with m being the width of the neural network and ϵ the approximation quality of the best critic neural network over the projected set.

Thisinteresting finding notonlyhighlights the potential redundancy of information added during fine-tuning, but also has significant implications for the multi-tenant serving and multi-tenant storage of fine-tuned models.

Natural language-conditioned reinforcement learning (RL) enables agents to follow human instructions.