Summary
Researchers have combined Stanford's ACE paper with the Reflective Language Model (RLM) pattern to create AI agents capable of writing code to analyze their own execution traces at scale. This approach leverages ACE's method of agents learning from execution feedback via an LLM-as-a-judge and curating strategies through in-context learning, without fine-tuning. The integration with RLM allows the LLM to write and execute code in a sandbox for more effective self-analysis and learning.
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