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The paper introduces TextReg, a regularization framework for text-space prompt optimization that mitigates prompt distributional overfitting via dual-evidence gradient purification and semantic edit regularization, yielding up to +11.8% over TextGrad and +16.5% over REVOLVE on out-of-distribution reasoning benchmarks.
Introduces DecoEvo, a score-decoupled co-evolution method for LLM optimization in text space that jointly improves solver and rubric-generator skills without gold rubrics, achieving 2.8–5.0% relative gains over baselines across five benchmarks.
Introducing SkillOpt, an optimizer that treats natural-language skills as trainable external parameters instead of finetuning model weights. It uses bounded edits and validation gating to enable stable, controllable skill updates, achieving best or tied-best results across 52 settings on 6 benchmarks with 7 models.