TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning
  for Enhancing LLMs' Social Intelligence
Authors: name: Guiyang Hou, name: Xing Gao, name: Yuchuan Wu, name: Xiang Huang, name: Wenqi Zhang, name: Zhe Zheng, name: Yongliang Shen, name: Jialu Du, name: Fei Huang, name: Yongbin Li, name: Weiming Lu

Recently, Large Language Models (LLMs) have made significant progress in
IQ-related domains that require careful thinking, such as mathematics and
coding. However, enhancing LLMs' cognitive development in social domains,
particularly from a post-training perspective, remains underexplored.
Recognizing that the social world follows a distinct timeline and requires a
richer blend of cognitive modes (from intuitive reactions (System 1) and
surface-level thinking to deliberate thinking (System 2)) than mathematics,
which primarily relies on System 2 cognition (careful, step-by-step reasoning),
we introduce Temporal-aware Hierarchical Cognitive Reinforcement Learning
(TimeHC-RL) for enhancing LLMs' social intelligence. In our experiments, we
systematically explore improving LLMs' social intelligence and validate the
effectiveness of the TimeHC-RL method, through five other post-training
paradigms and two test-time intervention paradigms on eight datasets with
diverse data patterns. Experimental results reveal the superiority of our
proposed TimeHC-RL method compared to the widely adopted System 2 RL method. It
gives the 7B backbone model wings, enabling it to rival the performance of
advanced models like DeepSeek-R1 and OpenAI-O3. Additionally, the systematic
exploration from post-training and test-time interventions perspectives to
improve LLMs' social intelligence has uncovered several valuable insights.