arXiv cs.AI· Pengyu Huang, Zhiyuan Han, Wenwen Tong, Hewei Guo, Jiangnan Chen, Sirui Chen, Lewei Lu, Beier Zhu, Xun Yang·· 11 小时前AI 评分31
CARE：面向共情强化学习的上下文自适应评分标准演化方法
Evolving Support Priorities in Empathetic Reinforcement Learning
AI 导读
针对共情强化学习中支持优先级随对话状态演变、而现有方法使用固定奖励规格的错配问题，研究者提出 Context-Adaptive Rubric Evolution（CARE），每轮动态调整认知、情感、主动共情三维权重及细粒度评估标准，作为在线共情 RL 的自适应奖励接口。
正文 · 原文
Abstract:We identify a fundamental mismatch in empathetic reinforcement learning: support priorities evolve with the dialogue state, yet existing methods typically optimize predefined reward specifications that remain fixed across turns. To model these evolving support priorities, we organize empathetic support along cognitive, affective, and proactive empathy, and propose Context-Adaptive Rubric Evolution (CARE). At each turn, CARE generates a context-adaptive rubric by adjusting both the weights of these three empathy dimensions and their fine-grained evaluation criteria. The rubric generator is trained with turn-level rubric supervision and human preference data through supervised fine-tuning followed by preference-based reinforcement learning, and then serves as an adaptive reward interface for online empathetic RL. Integrated with both RLVER and MICA, CARE achieves state-of-the-art performance across SentientBench, EQBench3, and EMPA under three independent LLM judges. Notably, on EMPA, CARE improves EPM-Idx over the strongest baseline by at least 13 points under all three judges, including an increase from 28.11 to 83.54 under Gemini-2.5-Pro. Further analyses show that learned rubric priorities systematically vary across dialogue stages and user emotions, demonstrating that CARE adapts what is rewarded as support needs evolve. Code is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.34249 [cs.AI] |
| (or arXiv:2609.34249v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.34249 arXiv-issued DOI via DataCite |
Submission history
From: Pengyu Huang [view email]
[v1]
Mon, 28 Sep 2026 03:55:21 UTC (3,598 KB)
[v2]
Wed, 14 Oct 2026 02:44:40 UTC (3,598 KB)
[v3]
Wed, 4 Nov 2026 10:29:50 UTC (4,467 KB)
来源：arXiv cs.AI · arxiv.org