TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning
  for Enhancing LLMs' Social Intelligence
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.
683fb063ef97de05eb2a44cc
Guiyang Hou
admin_assigned
2025-06-05T12:52:47.325Z
67c03110e8c7d56a8e135ac8
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/eP3y_8_tyB8tcrT7py4L7.png
Hou
user
Guiyang1001
683fb063ef97de05eb2a44cd
Xing Gao
683fb063ef97de05eb2a44ce
Yuchuan Wu
admin_assigned
2025-06-05T12:53:41.047Z
677f6b5883442ad1180a0087
/avatars/fe928cbed47b21439db90e75264e9a72.svg
yuchuan wu
user
yuchuan123
683fb063ef97de05eb2a44cf
Xiang Huang
683fb063ef97de05eb2a44d0
Wenqi Zhang
683fb063ef97de05eb2a44d1
Zhe Zheng
683fb063ef97de05eb2a44d2
Yongliang Shen
admin_assigned
2025-06-05T12:53:22.591Z
5e1058e9fcf41d740b69966d
/avatars/ce74839ba871f2b54313a670a233ba82.svg
Yongliang Shen
user
tricktreat
683fb063ef97de05eb2a44d3
Jialu Du
admin_assigned
2025-06-05T12:53:09.282Z
682e8a9b51706f69070c4b13
/avatars/0233c62d21bccc9de6816d74f78f6f38.svg
Jialu Du
user
dujialulu
683fb063ef97de05eb2a44d4
Fei Huang
683fb063ef97de05eb2a44d5
Yongbin Li
admin_assigned
2025-06-05T12:53:01.584Z
66641b2fd8e1e34bc621e688
https://cdn-avatars.huggingface.co/v1/production/uploads/66641b2fd8e1e34bc621e688/csPETwnx2zCIHSWi9uAi-.png
Yongbin Li
user
Yongbin-Li
683fb063ef97de05eb2a44d6
Weiming Lu
TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning
  for Enhancing LLMs' Social Intelligence
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.
Temporal-aware Hierarchical Cognitive Reinforcement Learning enhances LLMs' social intelligence by addressing the distinct cognitive demands of social domains.
Large Language Models Temporal-aware Hierarchical Cognitive Reinforcement Learning TimeHC-RL System 1 System 2 RL DeepSeek-R1 OpenAI-O3