title: TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning
    for Enhancing LLMs' Social Intelligence
summary: 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.
paper_authors._id: 683fb063ef97de05eb2a44cc
paper_authors.name: Guiyang Hou
paper_authors.status: admin_assigned
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paper_authors.fullname: Hou
paper_authors.type: user
paper_authors.user: Guiyang1001
paper_authors._id: 683fb063ef97de05eb2a44cd
paper_authors.name: Xing Gao
paper_authors._id: 683fb063ef97de05eb2a44ce
paper_authors.name: Yuchuan Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:53:41.047Z
paper_authors._id: 677f6b5883442ad1180a0087
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paper_authors.fullname: yuchuan wu
paper_authors.type: user
paper_authors.user: yuchuan123
paper_authors._id: 683fb063ef97de05eb2a44cf
paper_authors.name: Xiang Huang
paper_authors._id: 683fb063ef97de05eb2a44d0
paper_authors.name: Wenqi Zhang
paper_authors._id: 683fb063ef97de05eb2a44d1
paper_authors.name: Zhe Zheng
paper_authors._id: 683fb063ef97de05eb2a44d2
paper_authors.name: Yongliang Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:53:22.591Z
paper_authors._id: 5e1058e9fcf41d740b69966d
paper_authors.avatarUrl: /avatars/ce74839ba871f2b54313a670a233ba82.svg
paper_authors.fullname: Yongliang Shen
paper_authors.type: user
paper_authors.user: tricktreat
paper_authors._id: 683fb063ef97de05eb2a44d3
paper_authors.name: Jialu Du
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:53:09.282Z
paper_authors._id: 682e8a9b51706f69070c4b13
paper_authors.avatarUrl: /avatars/0233c62d21bccc9de6816d74f78f6f38.svg
paper_authors.fullname: Jialu Du
paper_authors.type: user
paper_authors.user: dujialulu
paper_authors._id: 683fb063ef97de05eb2a44d4
paper_authors.name: Fei Huang
paper_authors._id: 683fb063ef97de05eb2a44d5
paper_authors.name: Yongbin Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:53:01.584Z
paper_authors._id: 66641b2fd8e1e34bc621e688
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66641b2fd8e1e34bc621e688/csPETwnx2zCIHSWi9uAi-.png
paper_authors.fullname: Yongbin Li
paper_authors.type: user
paper_authors.user: Yongbin-Li
paper_authors._id: 683fb063ef97de05eb2a44d6
paper_authors.name: Weiming Lu
paper_title: TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning
    for Enhancing LLMs' Social Intelligence
paper_summary: 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.
paper_ai_summary: Temporal-aware Hierarchical Cognitive Reinforcement Learning enhances LLMs' social intelligence by addressing the distinct cognitive demands of social domains.
paper_ai_keywords: Large Language Models Temporal-aware Hierarchical Cognitive Reinforcement Learning TimeHC-RL System 1 System 2 RL DeepSeek-R1 OpenAI-O3