StopLoss: Teaching Agents to Predict Failure and Stop Early
Coding agents keep working on runs that will fail verification, and asking a frontier model to watch for this is expensive in its own right. We introduce StopLoss, a small external monitor that learns failure recognition from completed trajectories and their verifier outcomes. The name is inspired by the stop-loss order in finance: StopLoss ends a run early when the run is likely to fail, which saves resources. Final outcomes label earlier local windows, so a fine-tuned language model can score the most recent tool steps in a single forward pass, and a threshold calibrated on successful runs turns that score into an abort decision without changing the executor. Trained on GPT-family SWE-r...
| Authors | Jiarong Liang, Xueguang Ma, Dongfu Jiang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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