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cs.LG2024
Scaling Laws for Pre-training Agents and World Models
Tim Pearce, Tabish Rashid, Dave Bignell +3
The performance of embodied agents has been shown to improve by increasing model parameters, dataset size, and compute. This has been demonstrated in domains from robotics to video…
cs.LG2024
Reconciling Kaplan and Chinchilla Scaling Laws
Tim Pearce, Jinyeop Song
Kaplan et al. [2020] (`Kaplan') and Hoffmann et al. [2022] (`Chinchilla') studied the scaling behavior of transformers trained on next-token language prediction. These studies prod…
cs.LG2024
Diffusion for World Modeling: Visual Details Matter in Atari
Eloi Alonso, Adam Jelley, Vincent Micheli +4
World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner. Recent world models predominantly operate on sequence…