Summary of Regent: a Retrieval-augmented Generalist Agent That Can Act In-context in New Environments, by Kaustubh Sridhar et al.
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments
by Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, Insup Lee
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed approach pre-trains relatively small policies on relatively small datasets and adapts them to unseen environments via in-context learning, without any fine-tuning. A simple retrieval-based 1-nearest neighbor agent offers a surprisingly strong baseline for today’s state-of-the-art generalist agents. The semi-parametric agent, REGENT, trains a transformer-based policy on sequences of queries and retrieved neighbors, achieving significant performance with fewer parameters and pre-training data points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers proposes a new way to build artificial intelligence (AI) that can quickly adapt to new environments. They ask if making current AI architectures bigger is the best approach. Instead, they suggest training smaller AI models on smaller datasets and then letting them learn from their experiences in new situations. This helps them get better at doing tasks without needing to be retrained from scratch. The team also shows that using an “ask a friend” approach, where the AI asks for help from other AIs it knows, can be very effective. They call this approach REGENT and show it works well on different types of problems, such as robotics and game-playing. |
Keywords
» Artificial intelligence » Fine tuning » Nearest neighbor » Transformer
