STU-PID: Steering Token Usage via PID Controller for Efficient Large Language Model Reasoning

TL;DR

STUPID uses a PID controller to dynamically steer token usage in large language models, reducing redundancy and computational costs while improving reasoning accuracy.

Contribution

It introduces a training-free, adaptive method combining a classifier and PID control to optimize reasoning efficiency during inference.

Findings

Achieves 6% accuracy improvement on GSM8K

Reduces token usage by 32%

Outperforms static steering baselines

Abstract

Large Language Models employing extended chain-of-thought (CoT) reasoning often suffer from the overthinking phenomenon, generating excessive and redundant reasoning steps that increase computational costs while potentially degrading performance. While recent work has explored static steering approaches to mitigate this issue, they lack the adaptability to dynamically adjust intervention strength based on real-time reasoning quality. We propose STUPID (Steering Token Usage via PID controller), a novel training-free method that employs a PID controller to dynamically modulate activation steering strength during inference. Our approach combines a chunk-level classifier for detecting redundant reasoning patterns with a PID control mechanism that adaptively adjusts steering intensity based on the predicted redundancy probability. Experimental evaluation on GSM8K demonstrates that STUPID achieves a 6% improvement in accuracy while reducing token usage by 32%, outperforming static steering baselines. Our method provides a principled framework for dynamic reasoning calibration that maintains reasoning quality while significantly improving computational efficiency.