@inproceedings{zhu-etal-2026-eibench,
title = "{EIB}ench: A Simulator-Based Benchmark and Turn-Credit {RL} for Emotion Management",
author = "Zhu, Rongzhi and
Huang, Xiang and
Wu, Yuchuan and
Wang, Rui and
Sun, Zequn and
Ren, Tao and
Luo, Weiyao and
Qiu, Bingxue and
Ye, Jieping and
Li, Yongbin and
Hu, Wei",
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.789/",
pages = "16682--16698",
ISBN = "979-8-89176-462-0",
abstract = "Emotional intelligence (EI) in large language models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation. However, emotion management is interactive. A good response should improve the user{'}s emotional and relational state over several turns. We introduce EIBench, a simulator-based benchmark for interactive emotion management. EIBench contains 2,222 scenarios, with 2,009 for training and 213 for held-out testing. The scenarios are organized by a 2{\texttimes}2 taxonomy covering Support, Defense, Repair, and Charm. We evaluate 15 open- and closed-source LLMs. Current models perform well on support and rapport-building scenes, but struggle with boundary maintenance under user pressure. To improve the EI ability of LLMs, we propose Centered Turn-Credit GRPO (CTC-GRPO), a GRPO extension that reuses the simulator{'}s per-turn state updates as dense turn-level feedback while preserving the final outcome reward. CTC-GRPO improves Qwen3-8B from -22.4 to +22.4 on EIBench and also improves on out-of-distribution evaluations including SAGE (+12.4) and EQBench3 (+20.9{\%}). Our results show that simulator-tracked user states can support both evaluation and training for multi-turn emotion management."
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<abstract>Emotional intelligence (EI) in large language models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation. However, emotion management is interactive. A good response should improve the user’s emotional and relational state over several turns. We introduce EIBench, a simulator-based benchmark for interactive emotion management. EIBench contains 2,222 scenarios, with 2,009 for training and 213 for held-out testing. The scenarios are organized by a 2×2 taxonomy covering Support, Defense, Repair, and Charm. We evaluate 15 open- and closed-source LLMs. Current models perform well on support and rapport-building scenes, but struggle with boundary maintenance under user pressure. To improve the EI ability of LLMs, we propose Centered Turn-Credit GRPO (CTC-GRPO), a GRPO extension that reuses the simulator’s per-turn state updates as dense turn-level feedback while preserving the final outcome reward. CTC-GRPO improves Qwen3-8B from -22.4 to +22.4 on EIBench and also improves on out-of-distribution evaluations including SAGE (+12.4) and EQBench3 (+20.9%). Our results show that simulator-tracked user states can support both evaluation and training for multi-turn emotion management.</abstract>
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%0 Conference Proceedings
%T EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management
%A Zhu, Rongzhi
%A Huang, Xiang
%A Wu, Yuchuan
%A Wang, Rui
%A Sun, Zequn
%A Ren, Tao
%A Luo, Weiyao
%A Qiu, Bingxue
%A Ye, Jieping
%A Li, Yongbin
%A Hu, Wei
%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 zhu-etal-2026-eibench
%X Emotional intelligence (EI) in large language models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation. However, emotion management is interactive. A good response should improve the user’s emotional and relational state over several turns. We introduce EIBench, a simulator-based benchmark for interactive emotion management. EIBench contains 2,222 scenarios, with 2,009 for training and 213 for held-out testing. The scenarios are organized by a 2×2 taxonomy covering Support, Defense, Repair, and Charm. We evaluate 15 open- and closed-source LLMs. Current models perform well on support and rapport-building scenes, but struggle with boundary maintenance under user pressure. To improve the EI ability of LLMs, we propose Centered Turn-Credit GRPO (CTC-GRPO), a GRPO extension that reuses the simulator’s per-turn state updates as dense turn-level feedback while preserving the final outcome reward. CTC-GRPO improves Qwen3-8B from -22.4 to +22.4 on EIBench and also improves on out-of-distribution evaluations including SAGE (+12.4) and EQBench3 (+20.9%). Our results show that simulator-tracked user states can support both evaluation and training for multi-turn emotion management.
%U https://aclanthology.org/2026.findings-emnlp.789/
%P 16682-16698
Markdown (Informal)
[EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management](https://aclanthology.org/2026.findings-emnlp.789/) (Zhu et al., Findings 2026)
ACL
- Rongzhi Zhu, Xiang Huang, Yuchuan Wu, Rui Wang, Zequn Sun, Tao Ren, Weiyao Luo, Bingxue Qiu, Jieping Ye, Yongbin Li, and Wei Hu. 2026. EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 16682–16698, Budapest, Hungary. Association for Computational Linguistics.