MEMENTO: Memory-Guided Memetic Code-as-Policy Evolution

תקציר מקורי באנגליתarXiv:2607.22832v1 Announce Type: new Abstract: Long-horizon embodied tasks require policies that execute many dependent actions before task success can be observed. Representing policies as executable control pro- grams (code-as-policy) enables their decision logic to be inspected and revised after rollout evaluation. Revised programs can then be executed and compared by rollout performance, framing policy improvement as execution-guided program search. Evo- lutionary methods driven by large language models (LLMs) provide a natural mecha- nism for this search by generating variants and selecting high-performing candidates. However, existing approaches primarily select among independently generated vari- ants and lack a sequential local improvement phase. We introduce MEMENTO, a memory-gui