Two Experts Are All You Need for Steering Thinking: Reinforcing
  Cognitive Effort in MoE Reasoning Models Without Additional Training
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.
682db6f3975206d1caadf1cd
Mengru Wang
682db6f3975206d1caadf1ce
Xingyu Chen
682db6f3975206d1caadf1cf
Yue Wang
682db6f3975206d1caadf1d0
Zhiwei He
682db6f3975206d1caadf1d1
Jiahao Xu
682db6f3975206d1caadf1d2
Tian Liang
682db6f3975206d1caadf1d3
Qiuzhi Liu
682db6f3975206d1caadf1d4
Yunzhi Yao
682db6f3975206d1caadf1d5
Wenxuan Wang
682db6f3975206d1caadf1d6
Ruotian Ma
claimed_verified
2025-07-09T08:54:00.490Z
648294b2eb4befee378951c1
/avatars/da5d8bf9d8662cc2ffa2c0de49bd66a3.svg
Ruotian Ma
user
vvibt
682db6f3975206d1caadf1d7
Haitao Mi
682db6f3975206d1caadf1d8
Ningyu Zhang
claimed_verified
2025-05-21T12:26:50.565Z
620b3bbb0668e435407c8d0a
/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
user
Ningyu
682db6f3975206d1caadf1d9
Zhaopeng Tu
682db6f3975206d1caadf1da
Xiaolong Li
682db6f3975206d1caadf1db
Dong Yu
Two Experts Are All You Need for Steering Thinking: Reinforcing
  Cognitive Effort in MoE Reasoning Models Without Additional Training
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.
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.
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