Can We Steer Reasoning Direction by Thinking Intervention? (2025)

Abstract: Large Reason Models (LRMs) extend long reasoning process to solve complex tasks. However, due to the lack of fine-grained control, they often suffer from overthinking and erroneous reasoning problems, risking accuracy loss. To address this issue, we introduce Reasoning Direction Steering (RDS) to enable fine-grained control over LRMs’ reasoning behaviors by aligning reasoning trajectories with specific cognitive patterns. We develop a simple yet effective paradigm, Thinking Intervention, which explores two key dimensions - intervention positions and intervention styles - to achieve integration intervention throughout model reasoning processes. To validate the effectiveness of our approach, we conduct comprehensive experiments on multi-hop question answering tasks using state-of-the-art LRMs, including Qwen3-Series and R1-Series models. Experimental results demonstrate the efficacy of Thinking Intervention with 9.4% average improvement on R1-Series models and 1.9% improvement on Qwen3-Series models.

## introduction the advent of large reasoning language models (lrms) @xcite , exemplified by @xcite , @xcite , represents a paradigm shift in natural language processing, particularly in tackling sophisticated reasoning tasks @xcite . despite advances in training methodologies to incentivize model's thinking capability, the systematic control of their reasoning processes remains a fundamental challenge. lrms frequently exhibit overthinking tendencies @xcite , leading to inaccurate @xcite or obsolete information @xcite . current studies mainly focus on the implementation of constraints based on the final response of lrms, but * * corresponding authors these approaches lack the granularity necessary for precise control when faced with complex reasoning scenarios @xcite . such broad approaches fail to address the intricate relationship between reasoning processes and final outputs, short of meeting the high-precision requirements in real-world applications. this limitation is particularly critical, as the integrity of the reasoning process directly influences the accuracy and efficacy of the final output. to address these challenges, we introduces reasoning direction steering (rds), as shown in figure 1 . rds aims to dynamically guide models' reasoning trajectories to match users' unique cognitive patterns with decision-making processes. this mechanism ensures that lrms generate responses that not only maintain high accuracy but also naturally synchro-nize with users' individual thought frameworks and reasoning preferences. contemporary approaches to intervening in model thinking processes fall into two categories: prompt engineering-based interventions @xcite , exemplified by chain-of-thought (cot) @xcite , in-context learning (icl) @xcite , and experience-driven workflow designs, such as retrieval-augmented generation (rag) @xcite , and agent-based approaches @xcite . fundamentally, these methodologies share a common characteristic: they influence model reasoning by modifying the input structure rather than directly intervening in the reasoning process. in contrast to llms, a key distinguishing feature of lrms is their ability to generate an explicit and extended reasoning process prior to producing an answer, thereby making the implicit thought chains of llms observable and transparent. this characteristic naturally raises the question: can we directly intervene in the model's generated reasoning process to actively steer its direction, compelling the model to continue reasoning along a desired trajectory until the final answer is reached? such an approach has the potential to significantly enhance the correctness and effectiveness of the model's reasoning. compared to the aforementioned methods, it enables fine-grained and flexible control over the reasoning process by adaptively steering intermediate reasoning directions based on context-specific and task-specific needs. however, there remain two practical challenges in achieving rds. first, the reasoning processes generated by lrms often span several thousand or even tens of thousands of tokens, making it difficult to determine appropriate intervention positions for effective direction steering. second, each lrm possesses its own unique reasoning style, akin to individual human thinking habits. it may undermine the model's inherent reasoning abilities if the intervening information is inconsistent with the lrm's original reasoning style. therefore, it is crucial to identify both effective intervention positions and compatible intervention styles to ensure that interventions are seamlessly integrated, thereby preserving the model's inherent reasoning capabilities and effectiveness. to systematically address these challenges, we propose a novel thinking intervention paradigm that provides a principled framework for reasoning control. the proposed paradigm enables pre-cise identification of intervention positions and adaptive style matching to support dynamically adjust lrms' reasoning trajectories when chat with lrms. to achieve thinking intervention, we introduce two key dimensions: intervention position and intervention style. for the intervention position, we develop three strategies: stagebased, static-trigger, and dynamic-trigger, each designed to accommodate the model's inherent reasoning patterns. complementarily, the intervention style also encompasses three distinct fashions: vanilla, human-revised, and model-revised, which provide varying degrees of intervention expressiveness. through systematic exploration of these dimensions, we investigate how different combinations of position and style impact the effectiveness of interventions in steering model reasoning processes. to empirically validate the proposed thinking intervention, we implement and evaluate it under the setting of multi-hop question answering. while we measure the traditional final task accuracy, we also introduce a strengthen metric, reasoning accuracy (rea-acc), specifically designed to quantify the effectiveness of direction steering throughout the intermediate reasoning steps. experimental results demonstrate the effectiveness of the proposed thinking intervention framework across different model scales. compared to state-of-the-art baseline methods, our approach yields substantial improvements in both metrics: the deepseek distilled series models demonstrate an average performance gain of 9.4% in accuracy and 21.4% in rea-acc, while the qwen3 series models show mixed results with a slight increase of 1.9% in accuracy but a notable improvement of 15% in rea-acc. to further validate the framework's flexibility, we conducted extensive experiments varying both the intervention frequency and positioning across reasoning steps. the results confirm that our thinking intervention paradigm successfully enables precise directional adjustments throughout reasoning processes. in summary, this work makes the following key contributions: ## preliminaries and task formulation large reasoning models. large language models (llms) typically generate responses y directly from input x : @xmath0. in contrast to traditional llms, lrms employ special delimiters (〈think〉, 〈/think〉) to separate the reasoning processr from the final responsey: llm(x ) → ⟨think⟩r⟨/think⟩y lrms conduct systematic and thorough reasoning in r, and provide the final answer in y. reasoning direction: for complex problems, lrms generate multi-step reasoning processes @xmath1, . . . , s i , . . . ), where each reasoning step s i can be formalized as a triplet ⟨ps, t, pe⟩. here, ps denotes the initial state (representing the accumulated knowledge or problem understanding), pe the resulting state after reasoning, and t represents the reasoning process connecting these states. these states form connection points in a coherent reasoning path. the reasoning direction is thus defined as the state transition pair @xmath2, pe), which indicates that when the reasoning reaches state ps, it subsequently progresses toward state pe in the next reasoning step. ## novel paradigm: thinking intervention traditional approaches to intervention large language models' (llms) reasoning paths primarily rely on prompt engineering or task-specific workflows. however, lrms offer a more direct optimization pathway through the explicit reasoning processes itself. in this work, we propose a general paradigm termed thinking intervention, which directly intervenes within the reasoning process of lrms. formally defined as an intervention functionf: unlike traditional methods that merely prepend intervention information to input x , leaving the thinking process uncontrollable thinking intervention is a dynamic and precise intervention guidance during reasoning. within the thinking intervention paradigm, two critical factors determine intervention efficacy: position and style. position determines where in the reasoning process to intervene, targeting critical decision points for maximum impact. style dictates how information is presented to ensure the lrms effectively understanding intervention. therefore, we define our thought intervention framework along two dimensions: intervention position and intervention style, where each intervention strategy represents a combination of position and style. the overview of thinking intervention as shown in figure 2 , when the model's reasoning reaches the specified intervention position, the predetermined intervention information is inserted, thereby influencing the model's subsequent reasoning process until the final inference result. next, we separately introduce intervention positions(4.1) and intervention styles(4.2), and provide specific application scenarios and implementation detials in next section. ## instantiation: thinking intervention under multi-hop question answering to validate our research, we employ thinking intervention in multi-hop question answering (mqa) scenarios. following prior work @xcite ## conclusion we introduce the concept of reasoning direction steering for lrms, emphasizing the importance of fine-grained control over lrm reasoning. based on this, we introduced thinking intervention, a novel paradigm that actively controls the model's generated reasoning process, which offers a practical solution with high efficiency and performance. specifically, we develop an intervention framework that includes three types of intervention positions and three types of intervention styles, determining when and how to intervene. our method comprehensively evaluated across reasoning models of varying model sizes and demonstrates superior performance with fewer tokens on steering the reasoning direction. further analysis demonstrates thinking intervention has great potential for precise control of lrm reasoning.