Agent Workflows Are Now Being Search-Optimized
FinanceYF5 · x · 2026-07-13
This section highlights two main topics:
- Self-Taught Optimizer: When GPT-4 discovers optimization strategies like genetic algorithms or simulated annealing, it can use them to improve itself. However, swapping in weaker models like GPT-3.5 or Mixtral degrades performance, proving that recursive self-improvement requires a sufficiently strong base model.
- Workflow Search: ADAS allows a meta-agent to continuously propose, implement, and filter new agent architectures. AFlow represents workflows as graphs and optimizes them using Monte Carlo Tree Search, empirically outperforming both manual design and ADAS.
Related event: Lilian Weng's Deep Dive into Recursive Self-Improvement via Harness(9 posts)→
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