@leanxbt: This paper completely changed how I think about how an agent fixes its own code: Generate code -> Execute it -> Explain…

This paper completely changed how I think about how an agent fixes its own code: Generate code -> Execute it -> Explain the code line by line -> Spot the bug from the explanation and output -> Rewrite -> Repeat Here is the 5-step blueprint: Iterative debugging: the model does not write correct code from scratch, it enters a loop of edits to its own prediction. Rubber-duck: the model explains its generated code line by line in natural language and finds the bug from that explanation alone, with no error messages. Execution feedback: where unit tests or an interpreter exist, the real execution output is fed into the loop, not just self-explanation. Trace variant: the model dry-runs the code step by step in its head, which strengthens debugging where no tests are available. Reusing failures: failed predictions and their feedback are not thrown away but carried into the next round, sharply raising sample efficiency. Key insight: to find a bug the model needs no error message, it only needs to force itself to explain its own code in words. Self-Debugging matches or beats baselines that generate 10x more candidates, and lifts accuracy by up to 12%. Read this, then check the article below.