Persona generalization follows a shared line across Qwen and Llama
Original title: Is Persona Generalization Predictable?
Fine-tuning only later layers keeps strong in-domain learning while reducing out-of-domain unsafe transfer.
The paper reports an approximately probit-linear link between in-domain and out-of-domain persona rates after fine-tuning, including the unsafe behavior seen in emergent misalignment. That relationship is shared across hyperparameters and, surprisingly, across model families and sizes including Qwen and Llama. Learning rate, weight decay, and duration mostly move models along the same line. Changing which layers are trained can change the slope.
- The relationship is described as approximately probit-linear.
- Qwen and Llama share the reported line across families and sizes.
- Learning rate, weight decay, and duration mainly weaken in-domain learning.
- Later-layer-only fine-tuning yields shallower slopes for unsafe behavior.
- The authors frame this as a way to predict and control behavioral transfer.
From the original article
1 min read at arxiv.orgPersona generalization occurs when fine-tuning a language model in one domain induces behavioral changes in others -- for example, the broadly unsafe behavior observed in emergent misalignment.
By Olina Mukherjee · arxiv.org
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