Learning to Improve: Towards Learnable Recursive Self-Improvement for LLM Agents

The self-improvement of large language model agents is expanding from single response correction to continuous updating of reusable skills and execution strategies. However, the improvement of task ability does not necessarily lead to the improvement of improvement ability : when the diagnosis and repair mechanism remains fixed, it is difficult for the system to use its own repair experience to optimize the subsequent improvement process. In order to bridge this gap, this paper proposes a recursive self-improvement framework based on executable feedback, so that agents can not only update task skills, but also modify the programs that generate these updates. Specifically, the framework generates and evaluates candidate fixes by combining execution trajectories, verification results, and multi-dimensional skill diagnosis through the collaboration of task execution agents and enhanced agents. On this basis, the framework compares the expected effect and actual effect of repair, transforms the repeated program defects with clear judgment basis into executable regression constraints, and guides the enhanced agent to modify its own diagnosis, repair generation and self-modifying programs accordingly. The independently verified new version replaces the parent version to perform subsequent improvements, so that the improvement mechanism is not only the object of evolution, but also the executive subject of the next round of evolution. In addition, we introduce cumulative regression testing, version isolation, and uniform budget constraints to test the effectiveness of code evolution and trace its cost. TRACE connects task feedback and mechanism update into a verifiable recursive closed-loop, which provides a specific path for studying the continuous accumulation of agent improvement capabilities and cross-task migration.