The authors built a dual-agent framework for Meta-Black-Box Optimization, or MetaBBO, in which a learned policy controls design choices for a lower-level optimizer.
Self-modifying coding agents evolve learned optimizers from simple templates
The authors demonstrate a dual-agent system that iteratively rewrites both an optimization codebase and the agents directing its development.
Academic
Zipei Yu · Yue-Jiao Gong · Zeyuan Ma · Yuncheng Jiang · Zhiguang Cao
South China University of Technology · South China Normal University · Singapore Management Univeristy
Research Digest··2 min read
Yu et al.
Why this paper
From South China University of Technology and 2 others
In one line
HADA automates MetaBBO algorithm design using recursive self-improvement, outperforming human-designed baselines significantly.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
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