Reflective prompt optimization of a deep-research agent: a topic-disjoint, multi-seed protocol for measuring transferable gains and prompt-level overfitting
Read the original at arxiv.org→arXiv:2610.12601v1 Announce Type: new Abstract: Prompt optimization is an attractive alternative to reinforcement learning for adapting language-model agents, since it is cheap and produces human-readable artifacts....
Original headline: "Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting"
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- Oct 12, 04:00 UTC arXiv cs.CL lead source Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting