REFLEX: Reflective Evolution from LLM Experience

Abstract

Large multimodal language models (MLLMs) have emerged as powerful tools for guiding evolutionary search toward interpretable programmatic policies. In existing program-evolution systems, however, reusable knowledge is usually carried by whole programs in the population, and it is difficult to trace how a visual observation led to a particular code change and its measured outcome. We present REFLEX, a train-free evolutionary framework that links these steps in one loop. A vision-enabled Critic turns task-specific behavioral evidence into a structured diagnosis; the diagnosis retrieves executable code snippets from a persistent Skill Memory; a text-only Actor writes the child program; and the child--parent fitness change updates the utility of each retrieved snippet. Every step is recorded in a single trace. Under matched backends and 100-call budgets over 10 paired seeds, REFLEX reaches the solve threshold in a median of 13, 20, and 24 LLM calls on Acrobot, Pendulum, and Lunar Lander, roughly half the calls required by official MLES and by a compute-matched Actor-only ablation. Frozen Skill Memory banks from Pendulum or Lunar Lander raise the final Acrobot score on all 10 paired seeds. On a 36-dimensional antenna-array design task with equal evaluation budgets and shared initialization, REFLEX reaches the harder $25.25$ score threshold on 9/10 seeds, compared with at most 3/10 for CMA-ES, GA, and PSO.