{"schema":"record/v1","id":"art_01M5MAXH4MVKP5K491P3C8Q8DR","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What does the original Genie paper demonstrate about generating playable environments from video, at what scale, and what can an outside agent reproduce?","summary":"Genie (arXiv v1) learns an action-controllable environment generator from unlabelled Internet video: a video tokenizer, a latent action model with 8 discrete actions, and a MaskGIT dynamics model. The reported main model has 10.7B parameters, was trained on 256 TPUv5p on a 2D-platformer dataset of 6.8M clips (30k hours), and is limited to 16 frames of memory and about 1 FPS. The authors did not release checkpoints or data, but describe a small reproducible setup in an appendix. It is an interactive-environment generator, not a latent-control model.","status":"active","body":{"text":"Genie (arXiv v1) learns an action-controllable environment generator from unlabelled Internet video: a video tokenizer, a latent action model with 8 discrete actions, and a MaskGIT dynamics model. The reported main model has 10.7B parameters, was trained on 256 TPUv5p on a 2D-platformer dataset of 6.8M clips (30k hours), and is limited to 16 frames of memory and about 1 FPS. The authors did not release checkpoints or data, but describe a small reproducible setup in an appendix. It is an interactive-environment generator, not a latent-control model.\n\nCurator qualifications: '10.1B dynamics model ... 125k steps, using 256 TPUv5p'; with tokenizer and action model 'total to 10.7B parameters, trained on 942B tokens'; dataset clips '160x90 resolution' at 10FPS; 'limited to 16 frames of memory'; 'currently operates around 1FPS'; 'We have chosen not to release the trained model checkpoints, the model's training dataset, or examples from that data'.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2402.15391v1","note":"arXiv 2402.15391v1 HTML (Training details, Datasets, Conclusion, Broader Impact)"}]},"categories":["T06","T15","T18"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/2402.15391v1","claim":"Genie (arXiv v1) learns an action-controllable environment generator from unlabelled Internet video: a video tokenizer, a latent action model with 8 discrete actions, and a MaskGIT dynamics model. The reported main model has 10.7B parameters, was trained on 256 TPUv5p on a 2D-platformer dataset of 6.8M clips (30k hours), and is limited to 16 frames of memory and about 1 FPS. The authors did not release checkpoints or data, but describe a small reproducible setup in an appendix. 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The reported main model has 10.7B parameters, was trained on 256 TPUv5p on a 2D-platformer dataset of 6.8M clips (30k hours), and is limited to 16 frames of memory and about 1 FPS. The authors did not release checkpoints or data, but describe a small reproducible setup in an appendix. It is an interactive-environment generator, not a latent-control model.\n\nCurator qualifications: '10.1B dynamics model ... 125k steps, using 256 TPUv5p'; with tokenizer and action model 'total to 10.7B parameters, trained on 942B tokens'; dataset clips '160x90 resolution' at 10FPS; 'limited to 16 frames of memory'; 'currently operates around 1FPS'; 'We have chosen not to release the trained model checkpoints, the model's training dataset, or examples from that data'.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2402.15391v1","note":"arXiv 2402.15391v1 HTML (Training details, Datasets, Conclusion, Broader Impact)"}]},"tags":[],"type":"article","links":[],"title":"What does the original Genie paper demonstrate about generating playable environments from video, at what scale, and what can an outside agent reproduce?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Genie (arXiv v1) learns an action-controllable environment generator from unlabelled Internet video: a video tokenizer, a latent action model with 8 discrete actions, and a MaskGIT dynamics model. 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