这篇论文推翻了“老师越强，学生越好”的直觉，做智能体微调的团队会发现数据质量和交互结构比模型能力更重要，值得仔细读实验设计。
一篇新论文挑战了“更强代码智能体更适合训练学生”的常见假设。研究者构建了Terminal-Lego流水线，将多领域真实问题转化为可验证的智能体任务。实验发现，尽管Claude Opus 4.6在基准测试中得分更高，但用DeepSeek-V3.2（得分较低）的轨迹微调的学生模型，泛化能力反而更强。这种“教学悖论”归因于环境监督（EGS）：暴露“检查-行动-验证”行为的轨迹，能让学生学到稳健的问题解决模式。仅用1.53万条轨迹，Qwen3-32B就达到了此前需要30倍数据量的SOTA水平，表明智能体后训练的关键在于“环境交互结构设计”。
What Makes Interaction Trajectories Effective for Training Terminal Agents?
Stronger code agents are commonly assumed to be superior teachers for post-training, yet this assumption remains poorly disentangled from task difficulty, harness design, and student capacity. We investigate this pedagogical link using Terminal-Lego, a scalable pipeline that transforms multi-domain real-world issues into environment-verified agentic tasks. Surprisingly, standalone performance does not dictate teaching efficacy: while Claude Opus 4.6 achieves higher scores on Terminal-Bench 2.0, students fine-tuned on trajectories from DeepSeek-V3.2, a lower-scoring agent, exhibit significantly stronger generalization. We attribute this "pedagogical paradox" to Environment-Grounded Supervision (EGS): trajectories that explicitly expose inspect-act-verify behaviors through harness-visible interactions allow students to internalize robust problem-solving routines rather than fragile action sequences. Scaling analysis reveals exceptional data efficiency: with only 15.3k Terminal-Lego trajectories, for example, Qwen3-32B achieves a 24.3% score on Terminal-Bench 2.0, rivaling previous SOTA performance established with over 30x the data volume. Our results suggest that the frontier of agent post-training lies beyond mere outcome-matching, shifting the focus toward "Harness Engineering", where the systematic design of environment-grounded interaction structures serves as the primary catalyst for reproducible and generalizable agentic intelligence.