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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