研究团队表示，三款模型基于相同的基础训练数据集，高一致率的结果符合预期。真正具备研究价值的是模型间25%的分歧部分，这种差异大概率并非源于模型对工具质量的独立判断，而是由基于人类反馈的强化学习（RLHF）调优策略不同，以及生成环节的专属微调差异导致。

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总的来说，Claude Code的工具选择偏好不仅反映了当前AI技术在开发中的应用趋势，也为未来的开发模式提供了新思路。随着AI技术的不断发展，开发者和工具厂商都需要适应这一变化，抓住机遇，共同推动技术的进步与创新。 返回搜狐，查看更多 ...

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Kamal Mann is a Software Architect with over 22 years of experience in Industry 4.0 systems. He currently advises on edge ...