ResearchResearch paperTraining & Scaling · Reinforcement Learning · Efficiency & Inference1 source · Oct 16, 2026

An important Bottleneck in LLM Optimizers: Principle-Level Local Optima

Feedback-driven LLM optimizers are increasingly used for evolutionary search, Bayesian optimization, heuristic design, and scientific discovery.

We investigate principle-level search collapse, where an optimizer's search becomes confined to a narrow set of design principles, even though it may continue to generate distinct candidates and achieve small improvements.
We assemble a diagnostic benchmark spanning molecular optimization, network sentinel-node selection, and GPU-kernel optimization, and introduce a decision-tree protocol combining principle narrowing, prolonged performance plateaus, and gaps to attainable reference solutions.
We then test diversity injection, a common response to search stagnation, such as higher t, multiple LLMs, and web search, and show that it provides limited and inconsistent benefits: among 65 molecular runs, still 73.8% and 53.8% receive support and strong support respectively.
Our benchmark and diagnostic protocol provide a basis for evaluating future optimizers on principle-level collapse.

[1]An important Bottleneck in LLM Optimizers: Principle-Level Local Optima
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 10:39 PM
Feedback-driven LLM optimizers are increasingly used for evolutionary search, Bayesian optimization, heuristic design, and scientific discovery.
We investigate principle-level search collapse, where an optimizer's search becomes confined to a narrow set of design principles, even though it may continue to generate distinct candidates and achieve small improvements.

Extractive summary: sentences quoted from the sources.

Before this