Wait, We Don’t Need to “Wait”! Removing Thinking Tokens Improves Reasoning Efficiency (2025)

Abstract: Recent advances in large reasoning models have enabled complex, step-by-step reasoning but often introduce significant overthinking, resulting in verbose and redundant outputs that hinder efficiency. In this study, we examine whether explicit self-reflection, signaled by tokens such as “Wait” and “Hmm”, is necessary for advanced reasoning. We propose NoWait, a simple yet effective approach that disables explicit self-reflection by suppressing these tokens during inference. Extensive experiments on ten benchmarks across textual, visual, and video reasoning tasks show that NoWait reduces chain-of-thought trajectory length by up to 27%–51% in five R1-style model series, without compromising model utility. NoWait thus offers a plug-and-play solution for efficient and utility-preserving multimodal reasoning.

## introduction recent advancements in large reasoning models (lrms), exemplified by @xcite , have shown that complex reasoning abilities can be effectively elicited through simple rule-based reinforcement learning @xcite @xcite . these models produce explicit, step-by-step reasoning through long chain-of-thought (cot) trajectories @xcite ) before arriving at final answers. this capability is believed to be accompanied by the emergence of the "aha moment" phenomenon @xcite , in which the model begins to rethink problems and self-reflect on its reasoning trajectory with anthropomorphic expressions such as "wait", "hmm", or "alternatively". this was firstly achieved on r1-style language reasoning models and has been extended to vision-language models (vlms) @xcite ), † corresponding author. ‡ project leader. despite the effectiveness of long cot reasoning with self-reflection, the overthinking problem has emerged @xcite @xcite @xcite . it is characterized by excessively verbose reasoning and redundant thought steps, often extending over thousands of tokens, resulting in significant computational overhead and high reasoning latency. such inefficiencies hinder the practical deployment of r1-style reasoning models in applications with limited computational resources. although numerous efforts have been devoted to efficient reasoning, many existing approaches require additional training, either through reinforcement learning (rl) with length-based rewards @xcite @xcite or fine-tuning on variable-length cot trajectories @xcite . on the other hand, several training-free approaches have been proposed to mitigate overthinking by reducing token usage during inference. however, they often compromise the overall model utility @xcite or have only demonstrated effectiveness on distilled reasoning models @xcite . in this study, we investigate the impact of excessive self-reflection during the reasoning process and question whether explicit self-reflection, signaled by "wait"-like tokens, is really necessary for advanced reasoning. to this end, we propose nowait, a simple yet effective training-free approach that disables explicit self-reflection in r1style reasoning models, significantly reducing token usage while maintaining overall model utility. as illustrated in figure 1 , we directly intervene in the inference process by identifying specific keyword tokens (e.g., "wait", "hmm", and "alterna- ## preliminaries reasoning model generation patterns. reasoning models structure their output using thinking delimiters (i.e., <@xmath0, dividing the response into two main components: the cot trajectory detailing the reasoning process and the final answer summarizing overall thoughts. within the generated cots, models employ complex reasoning strategies, such as forward thinking, backtracking, and self-reflection. notably, large reasoning models often continue to reason even after obtaining an initial result, performing additional validation steps. accordingly, we define each segment of reasoning as a thinking chunk. each thinking chunk is associated with an intermediate answer r. formally, a thinking chunk can be represented as a pair (chunk i , r i ), where chunk i is the reasoning text and r i is the intermediate answer from chunk i derived from chunk i . thus, a complete cot can be structured as follows: the final response is the combination of the cot trajectory and a concise reasoning summary: self-reflection within reasoning models. as stated above, a single cot can contain multiple reasoning chunks. the transitions between these chunks are often marked by specific keywords, such as wait, alternatively, or hmm. models tend to switch their reasoning approaches in subsequent steps, often to verify previous results or explore alternative paths. however, this mechanism can sometimes lead to unproductive overthinking, causing models to repeatedly enter new reasoning steps and engage in redundant and unnecessary validation loops. in this study, we introduce nowait, a simple yet effective method for efficient reasoning by intervening in the generation of these keywords. this method alters models' self-reflection strategies and can be generalized to various modalities. ## removing thinking pattern is better in this section, we propose nowait, a simple yet effective method, that improves the reasoning efficiency while maintaining acceptable model utility. we first expand the method details in section 3.1, and introduce the experimental setup in section 3.2. we then report the experiment results on textual reasoning in section 3.3. additionally, we conduct the comparison experiment in section 3.4 and further analyze the generalization across different modalities ( visual reasoning and video reasoning ) in section 3.5. ## discussion in this section, we first discuss the effectiveness of our method nowait in section 4.1 by case study and the robustness of the model while applying nowait in section 4.2. additionally, we conduct an empirical experiment to analyze the difference between rl-based models and distill models based on nowait in section 4.3. ## related work large reasoning model the pursuit of advanced reasoning capabilities in large language models (llms) (openai, 2024) has spurred significant research, particularly focusing on strategies that scale computation @xcite or refine the generation process during inference. prior studies apply fundamental techniques like chain-of-thought (cot) prompting @xcite , guiding the model to think step by step, or integrate process reward models(prms), external verifiers, and search-guided decoding @xcite to aggregate multi-ple reasoning paths and enhance final answer accuracy. these efforts have culminated in a new generation of powerful large reasoning models (lrms), such as chatgpt-o1 (openai, 2024), deepseek-r1 (guo et al., 2025), qwq @xcite , gemini2.5 @xcite , which enable to spontaneous generation of extensive cot sequences involving forward thinking, backtracking, and verification steps. within the open-source domain, models derive reasoning abilities from diverse training paradigms, primarily through reinforcement learning (rl) @xcite @xcite on reasoning tasks or distillation @xcite on high-quality cot data produced from rlbased models. recent works @xcite have analyzed the difference between the two types of models. in this study, we include rl-based models for further exploration, underscoring the defects of rl-triggered reasoning capabilities. efficient reasoning while elaborating reasoning processes like long cot demonstrates enhanced performance on reasoning tasks, the associated verbosity presents a significant efficiency challenge @xcite . the generation of extensive intermediate steps substantially increase inference latency and computational cost, hindering practical deployment in real-world applications. consequently, a considerable body of work explores methods for efficient reasoning, aiming to reduce the length of reasoning traces without compromising accuracy. some techniques continue to train models for cot optimization @xcite @xcite , such as applying rl with length-based reward design @xcite @xcite , or fine-tuning with variable-length cot data @xcite @xcite . other methods conform training-free strategy, applying dynamic reasoning paradigms during inference @xcite @xcite or leveraging prompts to guide efficient reasoning @xcite @xcite . while existing studies are effective in cutting down the token usage, our study provides a new insight to rethink the internal mechanism of efficient reasoning and propose efficient multimodal reasoning. self-reflection & overthinking parallel to enhancing reasoning capabilities and efficiency, re-cent studies analyze the intricacies of the generated thought processes. within these generated sequences, an interesting phenomenon occurs -moments marked by keywords like "wait" and "hmm", which we term aha moment @xcite . these moments seemingly indicate a capability for self-reflection @xcite , allowing models to reassess their reasoning path and verify their cot before concluding. prior studies @xcite have begun to characterize these moments and probe the latent states to explore the potential mechanisms behind such spontaneous self-reflection. however, the frequent occurrence of these keywords can also lead to significant overthinking @xcite , where the model continues reflecting even after reaching correct intermediate or final conclusions. building on the initial characterizations from previous work, our study takes a further step to evaluate the functional effectiveness of these spontaneously generated aha moments, directly addressing whether they are essential contributors to the reasoning outcomes or potentially represent a form of inefficient behavioral mimicry. ## conclusion this work demonstrates that explicit self-reflection, signaled by tokens such as "wait" and "hmm", is not essential for advanced reasoning in r1-style models. by suppressing these tokens during inference, the proposed nowait approach effectively reduces overthinking and shortens chain-of-thought trajectories without compromising overall model utility. extensive experiments across diverse models and benchmarks in textual, visual, and video reasoning tasks demonstrate that nowait serves as an efficient and utility-preserving solution for multimodal reasoning, offering new insights for the lightweight deployment of large reasoning models. cannot comprehensively exhibit the reasoning capabilities of models from all aspects. preview by o1-pruner. this model can be accessed via hugging face.