The study analyzes the effectiveness of harness self-evolution in large language model (LLM) agents, revealing that models of varying capabilities produce similar updates but benefit differently from them. Mid-tier models show the most significant gains, suggesting that investment should focus on enhancing task-solving abilities rather than just the evolution process.