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However, post-training alignment operates on top of value structures already partially shaped during pretraining. Korbak et al. [35] show that language models implicitly inherit value tendencies from their training data, reflecting statistical regularities rather than a single coherent normative system. Related work on persona vectors suggests that models encode multiple latent value configurations or “characters” that can be activated under different conditions [26]. Extending this line of inquiry, Christian et al. [36] provides empirical evidence that reward models—and thus downstream aligned systems—retain systematic value biases traceable to their base pretrained models, even when fine-tuned under identical procedures. Post-training value structures primarily form during instruction-tuning and remain stable during preference-optimization [27].,推荐阅读豆包下载获取更多信息

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胡波,专栏作家,多年从业经验,致力于为读者提供专业、客观的行业解读。

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