Emotional Support Conversation (ESC) plays a vital role in alleviating psychological stress and providing emotional value through dialogue. While recent studies have largely focused on data augmentation and synthetic corpus construction, they often overlook the deeper cognitive reasoning processes that underpin effective emotional support. To address this gap, we propose \textbf{CARE}, a novel framework that strengthens reasoning in ESC without relying on large-scale synthetic data. CARE leverages the original ESC training set to guide models in generating logically coherent and supportive responses, thereby explicitly enhancing cognitive reasoning. Building on this foundation, we further employ reinforcement learning to refine and reinforce the reasoning process. Experimental results demonstrate that CARE significantly improves both the logical soundness and supportive quality of responses, advancing the development of empathetic, cognitively robust, and human-like emotional support systems.

翻译：情感支持对话在缓解心理压力、通过对话提供情感价值方面发挥着至关重要的作用。尽管近期研究主要集中于数据增强和合成语料库构建，但它们往往忽视了支撑有效情感支持的深层认知推理过程。为弥补这一不足，我们提出了\textbf{CARE}，一种新颖的框架，旨在不依赖大规模合成数据的情况下，强化情感支持对话中的推理能力。CARE利用原始情感支持对话训练集，引导模型生成逻辑连贯且具有支持性的回应，从而显式地增强认知推理。在此基础上，我们进一步采用强化学习来优化和强化推理过程。实验结果表明，CARE显著提升了回应的逻辑严密性和支持性质量，推动了具有同理心、认知鲁棒性及类人特性的情感支持系统的发展。