Summary of Aligning Agents Like Large Language Models, by Adam Jelley et al.
Aligning Agents like Large Language Models
by Adam Jelley, Yuhan Cao, Dave Bignell, Sam Devlin, Tabish Rashid
First submitted to arxiv on: 6 Jun 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 tackles the challenge of training agents to behave as desired in complex 3D environments from high-dimensional sensory information. Imitation learning from diverse human behavior provides a scalable approach, but may not result in the specific behaviors of interest. To address this issue, the authors draw an analogy between imitation learning agents and unaligned large language models (LLMs) and apply the procedure for aligning LLMs to aligning agents in a 3D environment. The study uses an academically illustrative part of a modern console game as a case study, demonstrating that it is possible to align an agent to consistently perform a desired mode of behavior. The authors provide insights and advice on successfully applying this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps train agents to behave correctly in complex video games from lots of sensory information. It’s hard to get the right behaviors just by copying what humans do, so the authors came up with a new way to align the agent’s behavior with what you want it to do. They used an example from a popular game and showed that it works! The idea is to make the agent behave more like how we want it to, rather than just copying random human actions. |
