The development of LLM-based tutor agents faces challenges in simultaneously ensuring adherence to pedagogical principles and achieving optimal pedagogical effectiveness, particularly in dynamic, multi-turn interactions.Existing methods are often constrained by static data or sparse reward signals in online settings.To address this gap, we propose Multi-Horizon Preference Optimization (MHPO), a novel framework that iteratively refines tutor agents using a multi-horizon reward function within a dynamic teacher-student simulation environment.Specifically, this reward function is designed to capture both turn-level pedagogical quality and trajectory-level pedagogical effectiveness, which is estimated via Monte Carlo rollouts.We further investigate two distinct strategies to aggregate these rewards for policy optimization.Our experiments demonstrate that MHPO significantly enhances base model performance, achieving a superior balance between principles and effectiveness compared to various baselines.

Role-playing agents (RPAs) have attracted growing interest for their ability to simulate immersive and interactive characters. However, existing approaches primarily focus on static role profiles, overlooking the dynamic perceptual abilities inherent to humans. To bridge this gap, we introduce the concept of dynamic role profiles by incorporating video modality into RPAs. To support this, we construct Role-playing-Video60k, a large-scale, high-quality dataset comprising 60k videos and 700k corresponding dialogues. Based on this dataset, we develop a comprehensive RPA framework that combines adaptive temporal sampling with both dynamic and static role profile representations. Specifically, the dynamic profile is created by adaptively sampling video frames and feeding them to the LLM in temporal order, while the static profile consists of (1) character dialogues from training videos during fine-tuning, and (2) a summary context from the input video during inference. This joint integration enables RPAs to generate greater responses. Furthermore, we propose a robust evaluation method covering eight metrics. Experimental results demonstrate the effectiveness of our framework, highlighting the importance of dynamic role profiles in developing RPAs.