Efficient Reasoning Through Suppression of Self-Affirmation Reflections in Large Reasoning Models

TL;DR

This paper identifies self-affirmation reflections as redundant steps in large reasoning models and demonstrates that suppressing them can significantly reduce output length without sacrificing accuracy, improving reasoning efficiency.

Contribution

The study uncovers the bias in leading words of self-affirmation reflections and introduces a train-free method to suppress these reflections, enhancing length compression in reasoning models.

Findings

Suppressing self-affirmation reflections reduces output length by up to 50.2%.

Suppression maintains accuracy across multiple models.

The method is simple, practical, and compatible with existing inference frameworks.

Abstract

While recent advances in large reasoning models have demonstrated remarkable performance, efficient reasoning remains critical due to the rapid growth of output length. Existing optimization approaches highlights a tendency toward "overthinking", yet lack fine-grained analysis. In this work, we focus on Self-Affirmation Reflections: redundant reflective steps that affirm prior content and often occurs after the already correct reasoning steps. Observations of both original and optimized reasoning models reveal pervasive self-affirmation reflections. Notably, these reflections sometimes lead to longer outputs in optimized models than their original counterparts. Through detailed analysis, we uncover an intriguing pattern: compared to other reflections, the leading words (i.e., the first word of sentences) in self-affirmation reflections exhibit a distinct probability bias. Motivated by this insight, we can locate self-affirmation reflections and conduct a train-free experiment demonstrating that suppressing self-affirmation reflections reduces output length without degrading accuracy across multiple models (R1-Distill-Models, QwQ-32B, and Qwen3-32B). Furthermore, we also improve current train-based method by explicitly suppressing such reflections. In our experiments, we achieve length compression of 18.7\% in train-free settings and 50.2\% in train-based settings for R1-Distill-Qwen-1.5B. Moreover, our improvements are simple yet practical and can be directly applied to existing inference frameworks, such as vLLM. We believe that our findings will provide community insights for achieving more precise length compression and step-level efficient reasoning.