This work proposes Effect-Marginal Policy Optimization (EMPO), which aligns policy optimization with execution-level equivalence through an effect-level surrogate that couples updates across alternative textual realizations, and shows that EMPO outperforms strong baselines in both task performance and learning efficien...
Shao-Jin Ma, Rui-Tao Wang, Peng-Yi Li et al.· 0 citations
PACE (Pre-execution Admission via Code Embeddings), an online-adaptive admission control framework that improves budgeted sample efficiency by estimating candidate utility prior to execution from within-run execution history, without training a separate offline predictor.
Gangyi Zhao, He-Bin Liang, Hongyao Tang et al.· Proceedings of the 32nd ACM...· 0 citations
Large Language Model (LLM)-driven AutoML agents have shown strong capabilities in constructing end-to-end machine learning pipelines. However, their effectiveness is limited by costly execution-based feedback, which can make the search for high-quality solutions inefficient under restricted computational budgets. We pr...
Gangyi Zhao, Hebin Liang, Hongyao Tang et al.· Proceedings of the 32nd ACM...· 0 citations
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