MIRROR: From Imitation to Internalization in LLM Personalization
The demand for personalized LLMs is shifting from style imitation toward content quality. We investigate whether self-distillation can bridge this gap in existing fine-tuning paradigm. To address this limitation, we introduce MIRROR(Meta- personalization by Internalizing Reference-Revealed On-policy Reflections), a novel self-distillation framework that shifts LLM personalization from imitation toward preference internalization. First, we replace reference-token imitation with reference-revealed on-policy self-distillation, aligning the model's next-token distributions along its own generation trajectories with those of its reference-conditioned self, thereby internalizing user preference...
| Authors | Huayi Lai, Jicheng Yang, Min Yi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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