CARE：认知推理增强型情感支持对话强化学习

摘要

情感支持对话（ESC）在通过交流缓解心理压力、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，却往往忽视了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这一创新框架，它无需依赖大规模合成数据，便能强化ESC中的推理能力。CARE巧妙利用原始ESC训练集，引导模型生成逻辑连贯且富有支持性的回应，从而显著提升认知推理水平。在此基础上，我们进一步采用强化学习技术，对推理过程进行优化与巩固。实验结果表明，CARE在提升回应的逻辑严密性与支持质量方面成效显著，推动了更具同理心、认知稳健且拟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.