Summary of Scaling Laws For Pre-training Agents and World Models, by Tim Pearce et al.
Scaling Laws for Pre-training Agents and World Models
by Tim Pearce, Tabish Rashid, Dave Bignell, Raluca Georgescu, Sam Devlin, Katja Hofmann
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the impact of scale on embodied agent performance in domains like robotics and video games. By pre-training generative learning objectives on large offline datasets and using imitation learning or world modeling, researchers have seen significant improvements in agent behavior. The study reveals that power laws, similar to those found in language modeling, exist in world modeling and imitation learning. However, the coefficients of these laws are influenced by factors like tokenizers, tasks, and architectures, which has implications for optimizing model sizes and data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how making models bigger and using more data can help embodied agents do better jobs. Researchers have already seen this work well in areas like robotics and video games. The study shows that there are rules about how performance improves as you add more model parts or data, but these rules depend on things like what kind of language the agent uses and what it’s trying to learn. |
