PID 控制启发的提示词自适应调度方法提出，应对噪声反馈下的持续更新
PID-Gated Continual Prompt Adaptation Under Noisy Feedback
arXiv 上的新论文，把 PID 控制用到提示词更新调度上，比 TextGrad、GEPA 更省调用次数，做智能体的可以看看。
论文针对 LLM 智能体在非平稳环境中指令漂移的问题，提出受 PID 控制启发的轻量级门控机制。该机制根据当前错误做出反应、随时间累积证据，并在编辑后回滚劣化，决定何时将反馈转化为持久的指令更新。作者将门控方法与简单基线及 TextGrad、GEPA 等提示词优化器对比，覆盖多种漂移任务、模型参数量、反馈噪声水平和随机种子。结果显示 PID 门控在任务准确率与编辑器调用预算之间取得比基线更优的 Pareto 权衡。
PID-Gated Continual Prompt Adaptation Under Noisy Feedback
Deploying LLM agents in non-stationary environments requires continually revising their instructions as the task drifts, yet rewriting after every reported mistake can turn noisy feedback into persistent errors. This raises an often-overlooked scheduling question in prompt adaptation: when should feedback trigger a lasting instruction update, and at what editor-call cost? Taking inspiration from proportional-integral-derivative (PID) control, we introduce a family of lightweight gates that react to the current error, accumulate evidence over time, and roll back deterioration after an edit. We benchmark these gates against both simple baselines and advanced prompt optimizers (TextGrad and GEPA), over a diverse suite of drifting tasks, LLM parameter sizes, feedback-noise levels, and random seeds. PID-gated continual prompt adaptation yields a more favorable Pareto trade-off between task performance (high accuracy, low error) and editor-call budget than the baselines considered, pointing to control-theoretic scheduling as a simple, general tool for robust prompt adaptation under unreliable feedback.
- DAIR.AI10-12 23:00原文
