CARE: Cognitive-reasoning Augmented Reinforcement for Emotional Support
  Conversation
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.
68e5e55c975ac4c405ef2131
Jie Zhu
claimed_verified
2025-10-08T05:49:16.107Z
642656cbad1e3b0e6e91b752
/avatars/3bf0ee15fd528e09b2b889f5cce3cbd0.svg
Jie Zhu
user
amazingj
68e5e55c975ac4c405ef2132
Yuanchen Zhou
68e5e55c975ac4c405ef2133
Shuo Jiang
68e5e55c975ac4c405ef2134
Junhui Li
68e5e55c975ac4c405ef2135
Lifan Guo
68e5e55c975ac4c405ef2136
Feng Chen
68e5e55c975ac4c405ef2137
Chi Zhang
68e5e55c975ac4c405ef2138
Fang Kong
CARE: Cognitive-reasoning Augmented Reinforcement for Emotional Support
  Conversation
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.
CARE is a framework that enhances cognitive reasoning in emotional support conversations through reinforcement learning, improving response quality and empathy without relying on large-scale synthetic data.
CARE reinforcement learning cognitive reasoning emotional support conversations logical soundness supportive quality empathetic cognitively robust human-like