Think on your Feet: Adaptive Thinking via Reinforcement Learning for
  Social Agents
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
681975a9fdcf582e6d0effdb
Minzheng Wang
claimed_verified
2025-05-06T08:33:29.678Z
64bcc373ef8c0e42bf16acc5
/avatars/873308203d28115ae1a9e4d0e26508f4.svg
mz.w
user
iiiiwis
681975a9fdcf582e6d0effdc
Yongbin Li
admin_assigned
2025-05-06T09:05:06.051Z
66641b2fd8e1e34bc621e688
https://cdn-avatars.huggingface.co/v1/production/uploads/66641b2fd8e1e34bc621e688/csPETwnx2zCIHSWi9uAi-.png
Yongbin Li
user
Yongbin-Li
681975a9fdcf582e6d0effdd
Haobo Wang
681975a9fdcf582e6d0effde
Xinghua Zhang
681975a9fdcf582e6d0effdf
Nan Xu
681975a9fdcf582e6d0effe0
Bingli Wu
admin_assigned
2025-05-06T09:05:37.861Z
668bd45044ab5de7e4c5b1e7
/avatars/9b087cfcac65a649a12568b601d5ca53.svg
bingli wu
user
bingliwu
681975a9fdcf582e6d0effe1
Fei Huang
681975a9fdcf582e6d0effe2
Haiyang Yu
681975a9fdcf582e6d0effe3
Wenji Mao
Think on your Feet: Adaptive Thinking via Reinforcement Learning for
  Social Agents
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
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.
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