Proactive dialogue has emerged as a critical and challenging research problem in advancing large language models (LLMs).Existing works predominantly focus on domain-specific or task-oriented scenarios, which leads to fragmented evaluations and limits the comprehensive exploration of models' proactive dialogue abilities.In this work, we propose Proac-tiveEval, a unified framework for evaluating proactive dialogue capabilities of LLMs.This framework decomposes proactive dialogue into target planning and dialogue guidance, establishing evaluation metrics across various domains.Moreover, it also enables the automatic generation of diverse and challenging evaluation data.Based on the proposed framework, we develop 328 evaluation environments spanning 6 distinct domains.Through experiments with 22 different types of LLMs, we show that DeepSeek-R1 and Claude-3.7-Sonnetexhibit exceptional performance on target planning and dialogue guidance tasks, respectively.Finally, we investigate how reasoning capabilities influence proactive behaviors and discuss their implications for future model development.Our code and data are available at the repository.

Alignment techniques enable Large Language Models (LLMs) to generate outputs that align with human preferences and play a crucial role in their effectiveness. However, their impact often diminishes when applied to Small Language Models (SLMs), likely due to the limited capacity of these models. Instead of directly applying existing alignment techniques to SLMs, we propose to utilize a well-aligned teacher LLM to guide the alignment process for these models, thereby facilitating the transfer of the teacher's knowledge of human preferences to the student model. To achieve this, we first explore a straightforward approach, Dual-Constrained Knowledge Distillation (DCKD), that employs knowledge distillation with two KL-divergence constraints from the aligned teacher to the unaligned student. To further enhance the student's ability to distinguish between preferred and dispreferred responses, we then propose Advantage-Guided Distillation for Preference Alignment (ADPA), which leverages an advantage function from the aligned teacher to deliver more nuanced, distribution-level reward signals for the student's alignment. Our experimental results show that these two approaches appreciably improve the alignment of SLMs and narrow the performance gap with larger counterparts. Among them, ADPA demonstrates superior performance and achieves even greater effectiveness when integrated with DCKD. Our code is available at https://github.com/SLIT-AI/ADPA .