AI RESEARCH
When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges
arXiv CS.AI
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ArXi:2605.26046v1 Announce Type: cross Customizing an LLM judge to a specific task or domain often involves optimizing its prompt across multiple evaluation criteria simultaneously. Textual gradient methods automate this for a single judge criterion, however they produce natural-language critiques, not numerical vectors. Thus, the conflict-resolution toolkit of multi-task learning (PCGrad, MGDA) doesn't apply to the multi-objective textual gradient setting.