Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Mengru, Chen, Xingyu, Wang, Yue, He, Zhiwei, Xu, Jiahao, Liang, Tian, Liu, Qiuzhi, Yao, Yunzhi, Wang, Wenxuan, Ma, Ruotian, Mi, Haitao, Zhang, Ningyu, Tu, Zhaopeng, Li, Xiaolong, Yu, Dong |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
by: Ji, Ke, et al.
Published: (2025)
by: Ji, Ke, et al.
Published: (2025)
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
by: Chen, Xingyu, et al.
Published: (2024)
by: Chen, Xingyu, et al.
Published: (2024)
Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms
by: Wang, Mengru, et al.
Published: (2025)
by: Wang, Mengru, et al.
Published: (2025)
Dancing with Critiques: Enhancing LLM Reasoning with Stepwise Natural Language Self-Critique
by: Li, Yansi, et al.
Published: (2025)
by: Li, Yansi, et al.
Published: (2025)
MoE Lens -- An Expert Is All You Need
by: Chaudhari, Marmik, et al.
Published: (2026)
by: Chaudhari, Marmik, et al.
Published: (2026)
DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning
by: He, Zhiwei, et al.
Published: (2025)
by: He, Zhiwei, et al.
Published: (2025)
Trust, But Verify: A Self-Verification Approach to Reinforcement Learning with Verifiable Rewards
by: Liu, Xiaoyuan, et al.
Published: (2025)
by: Liu, Xiaoyuan, et al.
Published: (2025)
Too Good to be Bad: On the Failure of LLMs to Role-Play Villains
by: Yi, Zihao, et al.
Published: (2025)
by: Yi, Zihao, et al.
Published: (2025)
Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
by: Wang, Yue, et al.
Published: (2025)
by: Wang, Yue, et al.
Published: (2025)
Steering MoE LLMs via Expert (De)Activation
by: Fayyaz, Mohsen, et al.
Published: (2025)
by: Fayyaz, Mohsen, et al.
Published: (2025)
Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs
by: Liu, Xiaoyuan, et al.
Published: (2024)
by: Liu, Xiaoyuan, et al.
Published: (2024)
DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural Language and Reinforcement Learning
by: Zhang, Ziyin, et al.
Published: (2025)
by: Zhang, Ziyin, et al.
Published: (2025)
EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models
by: Xu, Ziwen, et al.
Published: (2025)
by: Xu, Ziwen, et al.
Published: (2025)
Chain-of-Jailbreak Attack for Image Generation Models via Editing Step by Step
by: Wang, Wenxuan, et al.
Published: (2024)
by: Wang, Wenxuan, et al.
Published: (2024)
Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare
by: Shi, Zhengliang, et al.
Published: (2025)
by: Shi, Zhengliang, et al.
Published: (2025)
Unveiling the Pitfalls of Knowledge Editing for Large Language Models
by: Li, Zhoubo, et al.
Published: (2023)
by: Li, Zhoubo, et al.
Published: (2023)
Steer-MoE: Efficient Audio-Language Alignment with a Mixture-of-Experts Steering Module
by: Feng, Ruitao, et al.
Published: (2025)
by: Feng, Ruitao, et al.
Published: (2025)
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs
by: Zhou, Xin, et al.
Published: (2024)
by: Zhou, Xin, et al.
Published: (2024)
The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems
by: Ma, Xinbei, et al.
Published: (2025)
by: Ma, Xinbei, et al.
Published: (2025)
Draft Model Knows When to Stop: Self-Verification Speculative Decoding for Long-Form Generation
by: Zhang, Ziyin, et al.
Published: (2024)
by: Zhang, Ziyin, et al.
Published: (2024)
OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
by: Wang, Liujianfu, et al.
Published: (2025)
by: Wang, Liujianfu, et al.
Published: (2025)
Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training
by: Yuan, Youliang, et al.
Published: (2024)
by: Yuan, Youliang, et al.
Published: (2024)
Think Fast and Slow: Step-Level Cognitive Depth Adaptation for LLM Agents
by: Yang, Ruihan, et al.
Published: (2026)
by: Yang, Ruihan, et al.
Published: (2026)
Knowledge Circuits in Pretrained Transformers
by: Yao, Yunzhi, et al.
Published: (2024)
by: Yao, Yunzhi, et al.
Published: (2024)
From Data to Behavior: Predicting Unintended Model Behaviors Before Training
by: Wang, Mengru, et al.
Published: (2026)
by: Wang, Mengru, et al.
Published: (2026)
MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
by: Ma, Songkai, et al.
Published: (2025)
by: Ma, Songkai, et al.
Published: (2025)
BLR-MoE: Boosted Language-Routing Mixture of Experts for Domain-Robust Multilingual E2E ASR
by: Ma, Guodong, et al.
Published: (2025)
by: Ma, Guodong, et al.
Published: (2025)
Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics
by: Xu, Ziwen, et al.
Published: (2026)
by: Xu, Ziwen, et al.
Published: (2026)
Harder Tasks Need More Experts: Dynamic Routing in MoE Models
by: Huang, Quzhe, et al.
Published: (2024)
by: Huang, Quzhe, et al.
Published: (2024)
Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density
by: Mi, Zhendong, et al.
Published: (2026)
by: Mi, Zhendong, et al.
Published: (2026)
Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
by: Jaiswal, Ajay, et al.
Published: (2025)
by: Jaiswal, Ajay, et al.
Published: (2025)
Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
Guiding the Experts: Semantic Priors for Efficient and Focused MoE Routing
by: Min, Chengxi, et al.
Published: (2025)
by: Min, Chengxi, et al.
Published: (2025)
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
by: Tang, Yehui, et al.
Published: (2025)
by: Tang, Yehui, et al.
Published: (2025)
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
by: Shi, Chufan, et al.
Published: (2024)
by: Shi, Chufan, et al.
Published: (2024)
MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
by: Xia, Xinfeng, et al.
Published: (2025)
by: Xia, Xinfeng, et al.
Published: (2025)
SPC: Evolving Self-Play Critic via Adversarial Games for LLM Reasoning
by: Chen, Jiaqi, et al.
Published: (2025)
by: Chen, Jiaqi, et al.
Published: (2025)
ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
by: Xu, Yuhao, et al.
Published: (2026)
by: Xu, Yuhao, et al.
Published: (2026)
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
by: Zhang, Zhexiang, et al.
Published: (2025)
by: Zhang, Zhexiang, et al.
Published: (2025)
Cross-Platform Fused MoE Dispatch in Triton: Portable Expert Routing Without CUDA
by: Mitra, Subhadip
Published: (2026)
by: Mitra, Subhadip
Published: (2026)
Similar Items
-
The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
by: Ji, Ke, et al.
Published: (2025) -
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
by: Chen, Xingyu, et al.
Published: (2024) -
Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms
by: Wang, Mengru, et al.
Published: (2025) -
Dancing with Critiques: Enhancing LLM Reasoning with Stepwise Natural Language Self-Critique
by: Li, Yansi, et al.
Published: (2025) -
MoE Lens -- An Expert Is All You Need
by: Chaudhari, Marmik, et al.
Published: (2026)