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Naive agent-generated test feedback degrades SWE-bench performance by reinforcing shared hallucinations, but decoupling test generation from repair through role-specific RL converts a 3.9-point loss into an 11.4-point gain on open-weights models.
TRACE transforms how long-horizon agents are trained, leading to a remarkable performance increase on complex tasks without the need for supervised fine-tuning.
