MindChat-R0: A Large Language Model for Emotionally Supportive Dialogue through Reinforcement Learning

Emotional Support Conversation (ESC) systems are critical for assisting individuals facing mental health challenges. In this work, we present a reinforcement learning framework to improve ESC systems through structured emotional reasoning. We first collect and clean a dataset of 4,500 real-world support-seeking posts. To guide emotional generation, we introduce Empathetic Chain-of-Thought(ECoT), a structured reasoning format that encourages multi-turn empathy and coherence. Based on this, we train MindChat-R0 (Qwen3-8B as basic model), a Chinese empathetic dialogue agent, using reinforcement learning optimized by ECoT-driven reward signals. LLM-as-a-judge evaluation shows that MindChat achieves the highest average score of 3.863 out of 5.0 across fluency, empathy, and support dimensions (vs. 2.834 for Qwen3-8B-nothink and 2.547 for Qwen3-8B-think). In human preference evaluation, MindChat-R0 also outperforms strong baselines with a win rate of 71.14%, based on pairwise comparisons by human annotators.