RLVER: Reinforcement Learning with Verifiable Emotion Rewards for
  Empathetic Agents
Authors: name: Peisong Wang, name: Ruotian Ma, name: Bang Zhang, name: Xingyu Chen, name: Zhiwei He, name: Kang Luo, name: Qingsong Lv, name: Qingxuan Jiang, name: Zheng Xie, name: Shanyi Wang, name: Yuan Li, name: Fanghua Ye, name: Jian Li, name: Yifan Yang, name: Zhaopeng Tu, name: Xiaolong Li

Large language models (LLMs) excel at logical and algorithmic reasoning, yet
their emotional intelligence (EQ) still lags far behind their cognitive
prowess. While reinforcement learning from verifiable rewards (RLVR) has
advanced in other domains, its application to dialogue-especially for emotional
intelligence-remains underexplored. In this work, we introduce RLVER, the first
end-to-end reinforcement learning framework that leverages verifiable emotion
rewards from simulated users to cultivate higher-order empathetic abilities in
LLMs. Within this framework, self-consistent affective simulated users engage
in dialogue rollouts and produce deterministic emotion scores during
conversations, serving as reward signals to guide the LLM's learning.
Fine-tuning publicly available Qwen2.5-7B-Instruct model with PPO boosts its
Sentient-Benchmark score from 13.3 to 79.2 while largely preserving
mathematical and coding competence. Extensive experiments reveal that: (i)
RLVER consistently improves multiple dialogue capabilities; (ii) Thinking and
non-thinking models show distinct trends--thinking models excel in empathy and
insight, while non-thinking models favor action; (iii) GRPO often yields stable
gains, while PPO can push certain capabilities to a higher ceiling; (iv) More
challenging environments are not always better-moderate ones can yield stronger
outcomes. Our results show that RLVER is a practical route toward emotionally
intelligent and broadly capable language agents.