When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges 事件

When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges arXiv:2605.26046v1 Announce Type: new Abstract: 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)