Directly training AI agents with limited labeled video data
Directly training AI agents from scratch on small labeled video datasets is less effective than using an IDM.
Use the small labeled contractor dataset to train an Inverse Dynamics Model (IDM). Then, use the trained IDM to label a much larger dataset of unlabeled online videos. Finally, train the foundation model via behavioral cloning on this larger, IDM-labeled dataset.
Directly training a foundation model using behavioral cloning on a small dataset of labeled contractor data.
The IDM can leverage past and future information to predict actions, making the labeling task easier and requiring far less data than direct behavioral cloning. This approach allows for the utilization of vast amounts of unlabeled online video data, leading to more capable foundation models.
Directly training AI agents using behavioral cloning on small, labeled video datasets is inefficient. The recommended approach is to first train an Inverse Dynamics Model (IDM) on the labeled data, then use the IDM to label a larger unlabeled video dataset, and finally train the agent via behavioral cloning on the larger dataset.
- Learning to play Minecraft with Video PreTraining ↗OpenAI · OpenAI News · Jun 23, 2022 · paraphrased
First seen on Subvolts Jun 23, 2022. Updated Sep 11, 2026. Spotted a problem? Tell us.
- Learning to play Minecraft with Video PreTrainingOpenAI · OpenAI News · Jun 23, 2022
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- OpenAI (OpenAI News). Subvolts collected, normalized and credited it; the link above opens the original.