MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller
cs.AI, cs.LG
Submitted: 2026-09-29
Updated: 2026-09-29
Code: https://github.com/binbin2xs/MetaCtrl
Sources
- L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning
- SEAL: Steerable Reasoning Calibration of Large Language Models for Free
- Break the Chain: Large Language Models Can be Shortcut Reasoners
- Training Large Language Models to Reason in a Continuous Latent Space
- Measuring Mathematical Problem Solving With the MATH Dataset
- ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning
- AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting
- OpenAI o1 System Card
- How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach
- Stop When Further Reasoning Won't Help: Attention-State Adaptive Generation in Reasoning Models
- Reward-Guided Speculative Decoding for Efficient LLM Reasoning
- Understanding R1-Zero-Like Training: A Critical Perspective
- Reasoning Models Can Be Effective Without Thinking
- Humanity's Last Exam
- The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
- Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
- Chain of Draft: Thinking Faster by Writing Less
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