Beyond Full-Trajectory Imitation: Localized Expert Supervision for Issue Reproduction

Recent imitation learning methods improve code agents by learning from complete expert rollouts and expert continuations collected during student execution. However, effectively using such supervision requires identifying both where the student needs expert intervention and which expert behaviors are useful for correcting it. In this work, we present LoEC, a three-stage post-training framework that selectively allocates expert supervision for repository-level issue reproduction. LoEC first shapes the initial strategy through plan-level on-policy distillation, and then uses issue-reproduction progress signals to localize stalled or invalid states that require expert intervention. For successful expert recoveries, it further narrows supervision through intervention-level plan shaping and masked SFT over selected segments, including effective test edits retained in the final test. On SWT-Bench Verified, LoEC improves Pass@1 from 21.11% to 32.24%, and outperforms two expert-takeover imitation baselines. Compared with full-trajectory BC for initial alignment and full-suffix SFT for correction, LOEC achieves better performance while reducing supervised-token usage by over 91% and over 18%.