Self-Improving Large Language Models via Progressive Experience Evolution
SPEE is a unified post-training framework that sequentially performs explicit experience evolution followed by implicit policy optimization, and demonstrates that SPEE consistently outperforms both test-time and training-time self-evolution baselines across three model scales.