When More Experience Hurts: Diagnosing Experience Growth and Reader Alignment in Self-Evolving Language Agents

Experience-based self-evolution allows large language models (LLMs) to adapt after deployment without updating their parameters, while keeping accumulated experience readable and auditable. However, whether models can turn a growing memory bank into sustained improvement across model scales remains unclear. In this paper, we study these dynamics across four tasks and three representative strategies, evaluating successive memory checkpoints and varying the models that generate trajectories, edit memory, and use the resulting experience. We find that accumulating experience creates new successes without reliably preserving earlier ones, and that stronger experience sources do not consistently improve weaker readers. Controlled interventions show that the same experience can cause different losses across readers, while experience that helps alone can become ineffective alongside other entries. Guided by these findings, we explore explicit protection of experience applicability boundaries during reuse, with preliminary improvements suggesting a promising direction for more reliable self-evolution.