Two Experts Are All You Need for Steering Thinking: Reinforcing
  Cognitive Effort in MoE Reasoning Models Without Additional Training
Authors: name: Mengru Wang, name: Xingyu Chen, name: Yue Wang, name: Zhiwei He, name: Jiahao Xu, name: Tian Liang, name: Qiuzhi Liu, name: Yunzhi Yao, name: Wenxuan Wang, name: Ruotian Ma, name: Haitao Mi, name: Ningyu Zhang, name: Zhaopeng Tu, name: Xiaolong Li, name: Dong Yu

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.