Mitigating Overthinking in Large Reasoning Models via Manifold Steering

TL;DR

This paper introduces Manifold Steering, a novel technique to reduce overthinking in large reasoning models by projecting activation interventions onto a low-dimensional manifold, significantly decreasing computational overhead while maintaining accuracy.

Contribution

The paper reveals that overthinking is linked to a low-dimensional manifold in activation space and proposes a manifold projection method to effectively mitigate it.

Findings

Reduces output tokens by up to 71% on mathematical benchmarks

Maintains and improves accuracy across multiple tasks

Demonstrates robust transferability across domains

Abstract

Recent advances in Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex tasks such as mathematics and coding. However, these models frequently exhibit a phenomenon known as overthinking during inference, characterized by excessive validation loops and redundant deliberation, leading to substantial computational overheads. In this paper, we aim to mitigate overthinking by investigating the underlying mechanisms from the perspective of mechanistic interpretability. We first showcase that the tendency of overthinking can be effectively captured by a single direction in the model's activation space and the issue can be eased by intervening the activations along this direction. However, this efficacy soon reaches a plateau and even deteriorates as the intervention strength increases. We therefore systematically explore the activation space and find that the overthinking phenomenon is actually tied to a low-dimensional manifold, which indicates that the limited effect stems from the noises introduced by the high-dimensional steering direction. Based on this insight, we propose Manifold Steering, a novel approach that elegantly projects the steering direction onto the low-dimensional activation manifold given the theoretical approximation of the interference noise. Extensive experiments on DeepSeek-R1 distilled models validate that our method reduces output tokens by up to 71% while maintaining and even improving the accuracy on several mathematical benchmarks. Our method also exhibits robust cross-domain transferability, delivering consistent token reduction performance in code generation and knowledge-based QA tasks. Code is available at: https://github.com/Aries-iai/Manifold_Steering.