SWE-Loop: Trace-Guided Self-Improvement for Coding Agents

Coding agents are commonly improved by collecting additional trajectories or tuning a fixed task distribution, leaving open whether their own execution evidence can dynamically steer what they learn next. We present SWE-Loop, a trace-guided closed-loop framework for self-improving coding agents. At each iteration, SWE-Loop generates repository-level software-engineering tasks, obtains rollouts from a teacher model and the current student model, diagnoses semantically attributable failures, and updates the next task distribution to target recurring capability deficits. Supported by a multi-agent research team that validates tasks, curates trajectories, and controls the experimental process, SWE-Loop curates execution-verified trajectories as training data while analyzing teacher and student traces to identify shared blind spots and student-specific regressions. The framework disentangles semantic/model failures from system-level failures, including setup, provider, timeout, verifier, and infrastructure errors, and evaluates each iteration on a mainstream software-engineering benchmark and group-disjoint evaluation task families using resolution rate, patch coverage, regression rate, and compute cost. Across a three-iteration evolution loop, we demonstrate that trace-guided task synthesis yields generalizable improvements beyond non-adaptive baselines that train on self-generated tasks.