从反馈中进行 Persona Learning：可复用个性化 AI 助手框架 PLF
Persona Learning from Feedback for Personalized AI Assistants
AI 导读
arXiv 论文提出 Persona Learning from Feedback（PLF）框架，将基于偏好的可复用助手 persona 学习为共享冻结骨干上的独立 LoRA 适配器。
正文 · 原文
Abstract:Personalizing AI assistants requires adapting their behavior to users' preferences and task contexts. Existing work elicits assistant personas through persona prompting or finetuning on persona-conditioned generations. However, persona descriptions alone do not identify what to change or preserve in a particular response, or which behaviors suit the current request. We introduce $\textit{Persona Learning from Feedback (PLF)}$, a framework that learns reusable, preference-grounded assistant personas as independent LoRA adapters on a shared frozen backbone. During training, a frozen self-teacher conditions on persona specifications and response-specific feedback to provide token-level supervision on each adapter's own generated prefixes. A feedback gate focuses corrective updates on responses judged to need improvement. At deployment, a supervised router uses the request or dialogue to select a single adapter or a fixed composition, without user-specific finetuning. We evaluate PLF on single-turn personalization with model-generated critiques and multi-turn tutoring with simulated students' reactions. Across both settings, PLF improves model-judged preference fit over fixed-persona policies and strong baselines including persona prompting and persona distillation, demonstrating how feedback-trained behaviors can be reused for adaptive assistant personalization without degrading general capabilities.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.38561 [cs.LG] |
| (or arXiv:2610.38561v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38561 arXiv-issued DOI via DataCite |
Submission history
From: Yiran Shen [view email]
[v1]
Wed, 28 Oct 2026 09:55:26 UTC (289 KB)
来源：arXiv cs.AI · arxiv.org