We hope that BASALT will probably be utilized by anyone who goals to study from human feedback, whether they're working on imitation studying, studying from comparisons, or some other technique. Researchers are free to hardcode explicit actions at specific timesteps, or ask humans to supply a novel sort of suggestions, or practice a big generative mannequin on YouTube information, and so forth. This allows researchers to explore a much larger space of potential approaches to constructing helpful AI brokers. 4. Would the GPT-3 for Minecraft method work properly for BASALT? Is it sufficient to simply immediate the mannequin appropriately? For instance, a sketch of such an approach could be: – Create a dataset of YouTube videos paired with their routinely generated captions, and train a model that predicts the next video frame from earlier video frames and captions. Prepare a policy that takes actions which lead to observations predicted by the generative model (effectively learning to mimic human behavior, conditioned on previous video frames and the caption). This post is predicated on the paper The MineRL BASALT Competitors on Studying from Human Feedback, accepted on the NeurIPS 2021 Competitors Track. Since BASALT is sort of totally different from previous benchmarks, it permits us to review a wider number of research questions than we may earlier than.