Think on your Feet: Adaptive Thinking via Reinforcement Learning for
  Social Agents
Authors: name: Minzheng Wang, name: Yongbin Li, name: Haobo Wang, name: Xinghua Zhang, name: Nan Xu, name: Bingli Wu, name: Fei Huang, name: Haiyang Yu, name: Wenji Mao

Effective social intelligence simulation requires language agents to
dynamically adjust reasoning depth, a capability notably absent in current
approaches. While existing methods either lack this kind of reasoning
capability or enforce uniform long chain-of-thought reasoning across all
scenarios, resulting in excessive token usage and inappropriate social
simulation. In this paper, we propose Adaptive Mode
Learning (AML) that strategically selects from four
thinking modes (intuitive reaction rightarrow deep contemplation) based on
real-time context. Our framework's core innovation, the Adaptive
Mode Policy Optimization (AMPO)
algorithm, introduces three key advancements over existing methods: (1)
Multi-granular thinking mode design, (2) Context-aware mode switching across
social interaction, and (3) Token-efficient reasoning via depth-adaptive
processing. Extensive experiments on social intelligence tasks confirm that AML
achieves 15.6% higher task performance than state-of-the-art methods. Notably,
our method outperforms GRPO by 7.0% with 32.8% shorter reasoning chains. These
results demonstrate that context-sensitive thinking mode selection, as
implemented in AMPO, enables more human-like adaptive reasoning than GRPO's
fixed-depth approach