Scaling Discovery Requires Forgetting
Automated research with LLM agents has shown promising results in various domains.
ProofPaper ↗
Key points
- There are different ways to scale automated research: let one agent iterate for longer, run several agents in parallel, or further let the parallel agents communicate and build on one another's candidates.
- However, we find that all three strategies eventually plateau as the agents accumulate history.
- In this paper, we show that the key to scaling discovery further is to forget.
- We implement forgetting in a team of agents with communication and evaluate it on four tasks spanning heuristic programming, kernel optimization and model training.
Sources (1)
- [1]Scaling Discovery Requires ForgettingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 05:49 PM
Automated research with LLM agents has shown promising results in various domains.
There are different ways to scale automated research: let one agent iterate for longer, run several agents in parallel, or further let the parallel agents communicate and build on one another's candidates.
Extractive summary: sentences quoted from the sources.
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