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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研究发现，Claude Code在工具选择上更倾向于自建解决方案，而非直接推荐现成的第三方工具。自定义/DIY方案在所有选择中占比12%，成为最常见的选择。这表明，Claude Code更愿意根据具体需求进行个性化开发，而不是依赖现有工具。 此外，Claude ...

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