CARE: Cognitive-reasoning Augmented Reinforcement for Emotional Support
  Conversation
Authors: name: Jie Zhu, name: Yuanchen Zhou, name: Shuo Jiang, name: Junhui Li, name: Lifan Guo, name: Feng Chen, name: Chi Zhang, name: Fang Kong
Organization: Qwen DianJin

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.