title: Think on your Feet: Adaptive Thinking via Reinforcement Learning for
    Social Agents
summary: 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
paper_authors._id: 681975a9fdcf582e6d0effdb
paper_authors.name: Minzheng Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-06T08:33:29.678Z
paper_authors._id: 64bcc373ef8c0e42bf16acc5
paper_authors.avatarUrl: /avatars/873308203d28115ae1a9e4d0e26508f4.svg
paper_authors.fullname: mz.w
paper_authors.type: user
paper_authors.user: iiiiwis
paper_authors._id: 681975a9fdcf582e6d0effdc
paper_authors.name: Yongbin Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:05:06.051Z
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: 681975a9fdcf582e6d0effdd
paper_authors.name: Haobo Wang
paper_authors._id: 681975a9fdcf582e6d0effde
paper_authors.name: Xinghua Zhang
paper_authors._id: 681975a9fdcf582e6d0effdf
paper_authors.name: Nan Xu
paper_authors._id: 681975a9fdcf582e6d0effe0
paper_authors.name: Bingli Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:05:37.861Z
paper_authors._id: 668bd45044ab5de7e4c5b1e7
paper_authors.avatarUrl: /avatars/9b087cfcac65a649a12568b601d5ca53.svg
paper_authors.fullname: bingli wu
paper_authors.type: user
paper_authors.user: bingliwu
paper_authors._id: 681975a9fdcf582e6d0effe1
paper_authors.name: Fei Huang
paper_authors._id: 681975a9fdcf582e6d0effe2
paper_authors.name: Haiyang Yu
paper_authors._id: 681975a9fdcf582e6d0effe3
paper_authors.name: Wenji Mao
paper_title: Think on your Feet: Adaptive Thinking via Reinforcement Learning for
    Social Agents
paper_summary: 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
paper_ai_summary: A novel adaptive mode learning framework significantly enhances social intelligence simulation by dynamically selecting reasoning modes based on context, reducing token usage and improving performance compared to existing methods.
paper_ai_keywords: adaptive mode learning thinking modes deep contemplation adaptive mode policy optimization multi-granular design context-aware switching token-efficient reasoning depth-adaptive processing social intelligence tasks