CoDE-Stop: Early Stopping for Large Reasoning Models via Confidence Dynamics
A novel approach known as CoDE-Stop (Confidence Dynamics Early Stop) has been introduced to mitigate the high computational demands and performance issues linked to excessive reasoning in large models. The study, accessible on arXiv (2604.04930v2), highlights that accurate reasoning paths frequently arrive at high-confidence conclusions promptly, whereas incorrect attempts usually generate lengthy, unproductive reasoning sequences with unreliable confidence indicators. CoDE-Stop utilizes these confidence dynamics to determine the optimal point for halting reasoning, requiring no extra training and seamlessly fitting into current models. This method is designed to enhance both the efficiency and precision of chain-of-thought generation.
Key facts
- CoDE-Stop is an early stopping method for large reasoning models.
- It uses confidence dynamics of intermediate answers to decide when to stop reasoning.
- Correct trajectories often reach high-confidence answers early.
- Incorrect rollouts produce long, unproductive reasoning traces.
- The method requires no additional training.
- It integrates easily into existing models.
- The paper is available on arXiv with ID 2604.04930.
- The method addresses computational cost and performance degradation from overthinking.
Entities
Institutions
- arXiv