Hanjun Luo, Xuecheng Liu, Xiucheng Zhang, Zhuoning Xu, Yingbin Jin, Yankai Chen, Xue Liu, Hanan Salam · 2026 · arXiv
DOI: 10.48550/arXiv.2610.24949 · open-access PDF
编码代理人背后的语言模型是经过后训练的,结果奖励,最近的方法集中信誉决定结果的转折。我们发现,超越任务所需的防御行动逃脱两者。它们从沉默的叉子开始,每一个下一步的行动都导致同样的结果的转折。这些行动保持成功率不变,边界模型在默认预算下的决定性行动中占据了多达91%的比例。 最直接的修复,在轨道奖励中的检查者,只通过完全比例的轨道达到这些叉子。
Machine translation (Meta M2M100); the English original follows.
Language models behind coding agents are post-trained with outcome rewards, and recent methods concentrate credit on the turns that decide the outcome. We find that defensive actions beyond what a task requires escape both. They begin at silent forks, the turns where every next action leads to the same outcome. Such actions leave the success rate unchanged, and frontier models take the disproportionate branch there in up to 91% of decisive actions under implicit budgets. The most direct fix, a checker in the trajectory reward, reaches these forks only through fully proportionate trajectories,
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