DreamFormer first learns a task-agnostic Transformer world model from unstructured play data, then acquires task-specific behaviors by optimizing an intrinsic reward that aligns agent-generated rollouts with expert demonstrations within the latent imagination of a learned world model.
Mostafa Kotb, C. Weber, Muhammad Burhan Hafez et al.· 0 citations
In OpenWebText2 language modelling, LSTM-UT outperforms CoTFormer and, at equal width, reaches slightly lower perplexity than CoTFormer while CoTFormer needs up to 91% more training time per step; against a parameter-matched CoTFormer, LSTM-UT comes within 0.6 perplexity.
Depth-conditioned locomotion policies have demonstrated impressive agile maneuvers, but can be steered to unpredictable actions when observations are outside their training distribution. Occlusion, invalid returns, sensor noise, and visual distractors can shift deployment observations away from nominal simulated depth....
Natapat Kirdwichai, Tobias Driskell-Poole, Andrei Sontea et al.· 0 citations
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