CARE：情感支持对话中的认知推理增强强化学习

摘要

情感支持对话（ESC）在通过交流减轻心理压力与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与合成语料库构建，却常忽视支撑有效情感支持的深层认知推理过程。为填补这一空白，我们提出了CARE框架，该框架在不依赖大规模合成数据的前提下，强化了ESC中的推理能力。CARE利用原始ESC训练集引导模型生成逻辑连贯且具支持性的回应，从而显著提升认知推理。在此基础上，我们进一步采用强化学习来优化并巩固推理过程。实验结果表明，CARE显著提高了回应的逻辑严密性与支持质量，推动了具有同理心、认知稳健且类人化的情感支持系统的发展。

English

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 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.