title: Two Experts Are All You Need for Steering Thinking: Reinforcing
    Cognitive Effort in MoE Reasoning Models Without Additional Training
summary: Mixture-of-Experts (MoE) architectures within Large Reasoning Models (LRMs)
  have achieved impressive reasoning capabilities by selectively activating
  experts to facilitate structured cognitive processes. Despite notable advances,
  existing reasoning models often suffer from cognitive inefficiencies like
  overthinking and underthinking. To address these limitations, we introduce a
  novel inference-time steering methodology called Reinforcing Cognitive Experts
  (RICE), designed to improve reasoning performance without additional training
  or complex heuristics. Leveraging normalized Pointwise Mutual Information
  (nPMI), we systematically identify specialized experts, termed ''cognitive
  experts'' that orchestrate meta-level reasoning operations characterized by
  tokens like ''<think>''. Empirical evaluations with leading MoE-based LRMs
  (DeepSeek-R1 and Qwen3-235B) on rigorous quantitative and scientific reasoning
  benchmarks demonstrate noticeable and consistent improvements in reasoning
  accuracy, cognitive efficiency, and cross-domain generalization. Crucially, our
  lightweight approach substantially outperforms prevalent reasoning-steering
  techniques, such as prompt design and decoding constraints, while preserving
  the model's general instruction-following skills. These results highlight
  reinforcing cognitive experts as a promising, practical, and interpretable
  direction to enhance cognitive efficiency within advanced reasoning models.
paper_authors._id: 682db6f3975206d1caadf1cd
paper_authors.name: Mengru Wang
paper_authors._id: 682db6f3975206d1caadf1ce
paper_authors.name: Xingyu Chen
paper_authors._id: 682db6f3975206d1caadf1cf
paper_authors.name: Yue Wang
paper_authors._id: 682db6f3975206d1caadf1d0
paper_authors.name: Zhiwei He
paper_authors._id: 682db6f3975206d1caadf1d1
paper_authors.name: Jiahao Xu
paper_authors._id: 682db6f3975206d1caadf1d2
paper_authors.name: Tian Liang
paper_authors._id: 682db6f3975206d1caadf1d3
paper_authors.name: Qiuzhi Liu
paper_authors._id: 682db6f3975206d1caadf1d4
paper_authors.name: Yunzhi Yao
paper_authors._id: 682db6f3975206d1caadf1d5
paper_authors.name: Wenxuan Wang
paper_authors._id: 682db6f3975206d1caadf1d6
paper_authors.name: Ruotian Ma
paper_authors._id: 682db6f3975206d1caadf1d7
paper_authors.name: Haitao Mi
paper_authors._id: 682db6f3975206d1caadf1d8
paper_authors.name: Ningyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T12:26:50.565Z
paper_authors._id: 620b3bbb0668e435407c8d0a
paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_authors._id: 682db6f3975206d1caadf1d9
paper_authors.name: Zhaopeng Tu
paper_authors._id: 682db6f3975206d1caadf1da
paper_authors.name: Xiaolong Li
paper_authors._id: 682db6f3975206d1caadf1db
paper_authors.name: Dong Yu
paper_title: Two Experts Are All You Need for Steering Thinking: Reinforcing
    Cognitive Effort in MoE Reasoning Models Without Additional Training
paper_summary: Mixture-of-Experts (MoE) architectures within Large Reasoning Models (LRMs)
  have achieved impressive reasoning capabilities by selectively activating
  experts to facilitate structured cognitive processes. Despite notable advances,
  existing reasoning models often suffer from cognitive inefficiencies like
  overthinking and underthinking. To address these limitations, we introduce a
  novel inference-time steering methodology called Reinforcing Cognitive Experts
  (RICE), designed to improve reasoning performance without additional training
  or complex heuristics. Leveraging normalized Pointwise Mutual Information
  (nPMI), we systematically identify specialized experts, termed ''cognitive
  experts'' that orchestrate meta-level reasoning operations characterized by
  tokens like ''<think>''. Empirical evaluations with leading MoE-based LRMs
  (DeepSeek-R1 and Qwen3-235B) on rigorous quantitative and scientific reasoning
  benchmarks demonstrate noticeable and consistent improvements in reasoning
  accuracy, cognitive efficiency, and cross-domain generalization. Crucially, our
  lightweight approach substantially outperforms prevalent reasoning-steering
  techniques, such as prompt design and decoding constraints, while preserving
  the model's general instruction-following skills. These results highlight
  reinforcing cognitive experts as a promising, practical, and interpretable
  direction to enhance cognitive efficiency within advanced reasoning models.
paper_ai_summary: Reinforcing Cognitive Experts (RICE) improves reasoning performance and efficiency in Mixture-of-Experts architectures by identifying and utilizing specialized cognitive experts without requiring additional training or complex heuristics.
paper_ai_keywords: Mixture-of-Experts (MoE) Large Reasoning Models (LRMs) Reinforcing Cognitive Experts (RICE) normalized Pointwise Mutual Information (nPMI) meta-level reasoning cognitive efficiency overthinking underthinking prompt design decoding constraints cross-domain generalization