The core challenge of offline reinforcement learning (RL) is dealing with the (potentially catastrophic) extrapolation error induced by the distribution shift between the history dataset and the desired policy. A large portion of prior work tackles this challenge by implicitly/explicitly...

We introduce the ÌròyìnSpeech corpus -- a new dataset influenced by a desire to increase the amount of high quality, freely available, contemporary Yorùbá speech. We release a multi-purpose dataset that can be used for both TTS and ASR tasks. We curated text sentences from the news and creative...

Without proper safeguards, large language models will readily follow malicious instructions and generate toxic content. This motivates safety efforts such as red-teaming and large-scale feedback learning, which aim to make models both helpful and harmless. However, there is a tension between these...