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arXiv AI By Pengyu Huang, Zhiyuan Han, Wenwen Tong, Hewei Guo, Jiangnan Chen, Sirui Chen, Lewei Lu, Beier Zhu, Xun Yang

Evolving Support Priorities in Empathetic Reinforcement Learning

Read the original on arXiv AI →

The paper identifies a mismatch in empathetic reinforcement learning: current methods use fixed reward specifications while support priorities change with dialogue state. It introduces Context‑Adaptive Rubric Evolution (CARE), which dynamically adjusts weights for cognitive, affective, and proactive empathy at each turn, trained via supervised fine‑tuning and preference‑based reinforcement learning. CARE, integrated with RLVER and MICA, achieves state‑of‑the‑art results on SentientBench, EQBench3, and EMPA, notably boosting EPM‑Idx by up to 55 points under the Gemini‑2.5‑Pro judge.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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