做模型对齐和偏好优化的团队终于有了数据层面的诊断工具——不用再盲目调奖励权重，直接看数据教了模型什么。做安全对齐或模型人格定制的开发者建议点开，能省下大量试错时间。
这篇论文提出了一种基于可解释性的后训练数据管道，用于诊断和修正偏好数据中的虚假关联。作者通过可解释性协议识别出数据中潜在的概念，让用户能明确哪些行为应该被模型学习。实验表明，该方法能有效缓解过度风格化、谄媚等不良行为，并增强安全性和个性等期望属性。这项工作将后训练从优化黑箱奖励转变为审计和塑造学习信号的过程。
Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal
Language-model post-training is the main stage at which model behavior is shaped, yet it still largely involves optimization of scalar rewards that summarize diverse desiderata. This abstraction gives practitioners little visibility into what their data actually teaches models, allowing spurious correlations to be learned by a model and inducing undesirable behaviors such as over-stylization and sycophancy. To address this problem, we ask: can we inspect a preference dataset before optimization and decide, at the level of concepts, which behaviors a model should be allowed to learn? Motivated by this, we introduce a data-centric post-training pipeline that uses interpretability protocols to develop statistical hypotheses for the latent concepts separating preferred from dispreferred generations, making them explicit for fine-grained user feedback. Building on this view, we unify several interpretability-based training protocols as ways of shaping rewards via feature or data interventions. Empirically, we show that our pipeline diagnoses undesirable signals in existing preference data, mitigates off-target learning, and can also help amplify or shape desired properties such as safeguards and model personality. More broadly, our results suggest that interpretability can turn post-training from optimizing opaque proxy rewards into a process of auditing and sculpting the learning signal itself.