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This paper proposes a dual-loop self-evolution framework for multi-turn empathetic dialogue, using verifiable emotion feedback to optimize policy and adapt training distribution. On SAGE, it improves Qwen3-8B Overall from 53.87 to 79.24, outperforming uniform emotion-reward RL by 7.23 points.
STRIDE-ED is a strategy-grounded reasoning framework for empathetic dialogue systems that uses structured multi-stage reasoning combined with a data refinement pipeline and two-stage training (supervised fine-tuning + multi-objective RL) to improve emotional understanding and response generation. The framework demonstrates consistent improvements across open-source LLMs on both automatic metrics and human evaluations.