Beyond Data Quantity: From Bootstrap Emergence to Composition-Sensitive Self-Evolution

Self-evolving agents aim to reduce reliance on expert supervision by learning from their own interaction experience, yet it remains unclear how self-evolution emerges from an initially unreliable agent and what limits further improvement once executable self-generation becomes possible. We study this process in a controlled tool-augmented VideoQA setting with nested, task-balanced expert supervision and controlled within-task solution logic. Our experiments reveal two empirically distinct regimes. Before bootstrap, performance is strongly sensitive to expert supervision. A small increase in structured trajectories can transform an unreliable planner into one capable of executable self-generation. After bootstrap, further increases in expert trajectory quantity yield diminishing and strongly task-dependent gains, and this pattern persists after matching the optimization budget. We further characterize this post-bootstrap regime. Compared with continued expert-only training, self-generated experience produces a substantially broader set of successful tool-use patterns. At the action level, residual failures exhibit heterogeneous bottlenecks across generation, execution, and correct completion. Finally, controlled interventions show that repeatedly exposing the model to existing trajectories or applying coarse rebalancing provides limited benefit, whereas changing trajectory composition or fine-grained action-level supervision can alter the affected capability. Together, these results identify a transition from quantity-sensitive bootstrap emergence to composition-sensitive post-bootstrap learning, providing an empirical characterization of how tool-augmented self-evolution changes after it becomes executable.