@inproceedings{liu-etal-2026-escrag,
title = "{ESCRAG}-{R}1: Retrieval-Augmented Reinforcement Learning for Emotional Support Conversation",
author = "Liu, Weichu and
Hu, Yuxuan and
Sun, Yirong and
Mao, Ningning and
Zhang, Ziyun and
Chen, Jian and
Xu, Mingyang and
Zhong, Qishan and
Li, Chengming",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.346/",
pages = "7253--7266",
ISBN = "979-8-89176-462-0",
abstract = "Emotional Support Conversation (ESC) systems aim to provide holistic support by balancing professional therapeutic competence with natural empathy. However, existing methods struggle to simultaneously achieve structured, stage-aware reasoning and seamless empathy-expertise alignment, often resulting in an artificial splicing of clinical strategies and generic reassurance. To overcome these limitations, we propose $\textbf{ESCRAG-R1}$, a unified framework that integrates retrieval-based psychological guidance into Group Relative Policy Optimization (GRPO). By incorporating retrieval into the reinforcement learning loop, ESCRAG-R1 transforms external knowledge into a robust learning signal that stimulates explicit internal reasoning prior to generation and fundamentally reshapes the model{'}s internal policy. To provide the reliable supervision required for this optimization, we construct $\textbf{ESC-Preference}$, a high-quality dataset based on a Client{--}Counselor{--}Judge evaluation framework that delivers precise, empathy-aware reward signals. Extensive experiments demonstrate that ESCRAG-R1 significantly outperforms existing baselines by mitigating superficial splicing and realizing a natural integration of professional guidance and empathetic expression. Code and datasets will be publicly released at https://anonymous.4open.science/r/ESCRAG-R1-83F1/."
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<abstract>Emotional Support Conversation (ESC) systems aim to provide holistic support by balancing professional therapeutic competence with natural empathy. However, existing methods struggle to simultaneously achieve structured, stage-aware reasoning and seamless empathy-expertise alignment, often resulting in an artificial splicing of clinical strategies and generic reassurance. To overcome these limitations, we propose ESCRAG-R1, a unified framework that integrates retrieval-based psychological guidance into Group Relative Policy Optimization (GRPO). By incorporating retrieval into the reinforcement learning loop, ESCRAG-R1 transforms external knowledge into a robust learning signal that stimulates explicit internal reasoning prior to generation and fundamentally reshapes the model’s internal policy. To provide the reliable supervision required for this optimization, we construct ESC-Preference, a high-quality dataset based on a Client–Counselor–Judge evaluation framework that delivers precise, empathy-aware reward signals. Extensive experiments demonstrate that ESCRAG-R1 significantly outperforms existing baselines by mitigating superficial splicing and realizing a natural integration of professional guidance and empathetic expression. Code and datasets will be publicly released at https://anonymous.4open.science/r/ESCRAG-R1-83F1/.</abstract>
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%0 Conference Proceedings
%T ESCRAG-R1: Retrieval-Augmented Reinforcement Learning for Emotional Support Conversation
%A Liu, Weichu
%A Hu, Yuxuan
%A Sun, Yirong
%A Mao, Ningning
%A Zhang, Ziyun
%A Chen, Jian
%A Xu, Mingyang
%A Zhong, Qishan
%A Li, Chengming
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-escrag
%X Emotional Support Conversation (ESC) systems aim to provide holistic support by balancing professional therapeutic competence with natural empathy. However, existing methods struggle to simultaneously achieve structured, stage-aware reasoning and seamless empathy-expertise alignment, often resulting in an artificial splicing of clinical strategies and generic reassurance. To overcome these limitations, we propose ESCRAG-R1, a unified framework that integrates retrieval-based psychological guidance into Group Relative Policy Optimization (GRPO). By incorporating retrieval into the reinforcement learning loop, ESCRAG-R1 transforms external knowledge into a robust learning signal that stimulates explicit internal reasoning prior to generation and fundamentally reshapes the model’s internal policy. To provide the reliable supervision required for this optimization, we construct ESC-Preference, a high-quality dataset based on a Client–Counselor–Judge evaluation framework that delivers precise, empathy-aware reward signals. Extensive experiments demonstrate that ESCRAG-R1 significantly outperforms existing baselines by mitigating superficial splicing and realizing a natural integration of professional guidance and empathetic expression. Code and datasets will be publicly released at https://anonymous.4open.science/r/ESCRAG-R1-83F1/.
%U https://aclanthology.org/2026.findings-emnlp.346/
%P 7253-7266
Markdown (Informal)
[ESCRAG-R1: Retrieval-Augmented Reinforcement Learning for Emotional Support Conversation](https://aclanthology.org/2026.findings-emnlp.346/) (Liu et al., Findings 2026)
ACL
- Weichu Liu, Yuxuan Hu, Yirong Sun, Ningning Mao, Ziyun Zhang, Jian Chen, Mingyang Xu, Qishan Zhong, and Chengming Li. 2026. ESCRAG-R1: Retrieval-Augmented Reinforcement Learning for Emotional Support Conversation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 7253–7266, Budapest, Hungary. Association for Computational Linguistics.