Large Language Model (LLM) personalization aims to adapt generation to individual users by capturing implicit preferences from historical interactions. Steering-based methods offer an inference-efficient solution, but existing work follows a difference-based paradigm that estimates personalized steering directions from representation offsets between contrastive historical examples, thus entangling inherent user preference with preference-agnostic contextual bias. This limitation arises because offset-based estimation uses raw contrastive variation, making the derived direction confounded by contextual bias rather than identifying preference-discriminative variation between user-conditioned and generic behavior. To address this, we propose Disentangled Personal Direction Discovery (DiPer), which formulates personalized steering vector estimation as a discriminative direction discovery problem. DiPer learns layer-wise user-specific directions through supervised metric learning, based on Recursive Feature Machine (CITATION), over historical activations, to discover preference-discriminative directions while suppressing contextual bias. Theoretical analysis and extensive experiments demonstrate DiPer’s efficacy and robustness.

Large language models (LLMs) have greatly advanced conversational systems, making Personalized Dialogue Generation (PDG) a core direction for tailoring responses to individual users. However, retrieval-augmented and user-parameterization approaches typically rely on dense user-level persona information and consistent domain-level persona distributions, leading to two key challenges for scalable PDG: sparse personas of unseen users hinder personalization, while domain-bound user parameters require costly retraining across dialogue scenes. Therefore, we argue that these limitations originate from an over-reliance on static data fitting, as opposed to mastering atomic personalization capabilities. To this end, we propose Meta-Collaborative Learning (MCL), a novel learning paradigm that proactively supplements cues via Neighbor Identifier and Booster, and dynamically aligns responses to preferences by Generator. By decomposing PDG into synergistic modules with bi-level joint optimization, MCL internalizes universal personalization skills for acquiring and leveraging contextual signals across diverse users and domains. Extensive experiments validate that MCL delivers robust personalization under practical unseen-user and cross-domain settings.

Gradient-free prompt optimization methods have made significant strides in enhancing the performance of closed-source Large Language Model (LLMs) across a wide range of tasks. However, existing approaches make light of the importance of high-quality prompt initialization and the identification of effective optimization directions, thus resulting in substantial optimization steps to obtain satisfactory performance. In this light, we aim to accelerate prompt optimization process to tackle the challenge of low convergence rate. We propose a dual-phase approach which starts with generating high-quality initial prompts by adopting a well-designed meta-instruction to delve into task-specific information, and iteratively optimize the prompts at the sentence level, leveraging previous tuning experience to expand prompt candidates and accept effective ones. Extensive experiments on eight datasets demonstrate the effectiveness of our proposed method, achieving a consistent accuracy gain over baselines with less than five optimization steps.