video1.0<iframe src="https://www.loom.com/embed/ee0636cad72b4a9c9181acf7758f798f" frameborder="0" width="1110" height="832" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>8321110Loomhttps://www.loom.com8321110https://cdn.loom.com/sessions/thumbnails/ee0636cad72b4a9c9181acf7758f798f-8e33c3493c116443.gif64.212This Loom explains how RLForge automatically generates reinforcement learning environments from provided materials like a tag drawing or a math or programming paper. Users can select a paper, and when it runs, it generates 20 problems to solve while showing the resulting environment. The viewer can download source artifacts and the generated environment. The creator notes that these environments are useful for digitizing knowledge, and mentions achieving about 15% holdout accuracy for math on undigitized or less common papers.