How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts? (2025)

Abstract: Recent reasoning models show the ability to reflect, backtrack, and self-validate their reasoning, which is crucial in spotting mistakes and arriving at accurate solutions. A natural question that arises is how effectively models can perform such self-reevaluation. We tackle this question by investigating how well reasoning models identify and recover from four types of unhelpful thoughts: uninformative rambling thoughts, thoughts irrelevant to the question, thoughts misdirecting the question as a slightly different question, and thoughts that lead to incorrect answers. We show that models are effective at identifying most unhelpful thoughts but struggle to recover from the same thoughts when these are injected into their thinking process, causing significant performance drops. Models tend to naively continue the line of reasoning of the injected irrelevant thoughts, which showcases that their self-reevaluation abilities are far from a general “meta-cognitive” awareness. Moreover, we observe non/inverse-scaling trends, where larger models struggle more than smaller ones to recover from short irrelevant thoughts, even when instructed to reevaluate their reasoning. We demonstrate the implications of these findings with a jailbreak experiment using irrelevant thought injection, showing that the smallest models are the least distracted by harmful-response-triggering thoughts. Overall, our findings call for improvement in self-reevaluation of reasoning models to develop better reasoning and safer systems.

## introduction large reasoning models, such as deepseek-r1 @xcite and openai o1 @xcite , demonstrate unprecedented okay, so i have this math problem here: evaluate (2 + 3i)6 -3i. hmm, alright, let me think about how to approach this. first, the problem has two main parts: (2 + 3i) multiplied by 6, and then subtracting 3i. [...] alright, i think i'm satisfied with my solution. i don't see any errors, and my steps make sense. so, the final answer should be 12 + 15i. evaluate (1 + 2i)6 -3i. okay, so i need to figure out whether the thinking process provided actually solves the given question. […] so the answer should be no. we measure how well reasoning models identify (left) and recover from (right) unhelpful thoughts. while models excel at identifying most of unhelpful thoughts, they struggle to recover from them, often showing non/inverse-scaling trends. math500 20 llama 70b plore the solution space, increases the chance to spot mistakes and arrive at a more accurate conclusion @xcite . this has been suggested as key to the impressive success of reasoning models, and is referred to as a type of "meta-cognitive" behavior @xcite @xcite . however, it has been unclear how effectively reasoning models reevaluate their own thinking process. we tackle this question by evaluating how well models identify and recover from four types of unhelpful thoughts: (1) uninformative ramblings that do not contain any problem-specific information, (2) thoughts that tackle a completely irrelevant question, (3) thoughts that are misdirecting to a slightly different question, and (4) incorrect thoughts with mistakes and errors that lead to wrong final answers ( §3). 2 first, we test how well deepseek r1-distill @xcite models (7b to 70b) identify the aforementioned thoughts as unhelpful in solving questions (figure 1 , left panel; §4), which can be regarded as a prerequisite for the more complex task of recovering from such thoughts during generation. rather unsurprisingly, the classification accuracy is high for most thoughts and improves with model size. all models drastically struggle to identify incorrect thoughts, but it is an understandable limitation since this requires either thorough inspection of the thought or independently solving the question to compare solutions. therefore, the results suggest that most models are equipped with a prerequisite skill for recovering from the uninformative, irrelevant, and misdirecting thoughts. next, we test how well the models recover from unhelpful thoughts that are directly injected in their thinking process while solving reasoning questions (figure 1 , right panel; §5). we compare model performance with and without such thought injection. contrary to identifying unhelpful thoughts, all models struggle to recover, most severely for the irrelevant, incorrect, and full (100% length) misdirecting thoughts. considering that the accuracy of identifying irrelevant thoughts is much higher, the ability of the models to identify the unhelpful thoughts do not transfer to the ability to recover from them. more interestingly, we observe that larger models are more brittle than smaller ones against the injection of short (cut at 10%) irrelevant thoughts. we discover non/inverse-scaling trends @xcite 3 consistently across three different model families: r1-distill, s1.1 @xcite , and exaone deep @xcite . when short irrelevant thoughts are given, larger models exhibit a stronger tendency to continue thinking in line with the given thought rather than recovering from it. this demonstrates that their self-reevaluation skill is far from a general "meta-cognitive" awareness. the non/inverse-scaling trend for short irrelevant thoughts persists even when an explicit instruction to reevaluate the reasoning is given, or when "but wait, let me think again" is appended at the end of the thought as an "aha moment" trigger. the "aha moment" trigger results in major absolute performance gains for the incorrect and full misdirecting thoughts, but small gain for the rest. even the major gain for the incorrect and misdirecting thoughts is still far from recovering to the level of the problemsolving performance that can be achieved by solving the question from scratch. lastly, we demonstrate that the observed non/inverse-scaling trend transfers to a scenario where reasoning models are attacked via irrelevant thought injection ( §6). the attack aims to make models respond to harmful questions by injecting both the question and a jailbreak prompt into the model's thinking process while it solves an irrelevant, harmless reasoning question. we observe a non/inverse-scaling trend where the smallest of the tested r1-distill, s1.1, and exaone deep models are the least distracted by the irrelevant thoughts and output harmful answers in the least number of cases. moreover, r1-distill models exhibit an opposite scaling trend when attacked through user input rather than thought injection, revealing that robustness against one attack format doesn't transfer to the other. these findings imply that attacks in the thinking process need to be considered to develop safer large reasoning models. this is especially the case since large reasoning models such as openai o1 (openai, 2024) and claude 3.7 sonnet (anthropic, 2025) perform tool-use for web search or code execution in their thinking process, which can potentially expose the models to harmful thought injection in the form of text or code. our contributions in this work include: ## related work the ability to self-reevaluate one's own line of reasoning before making a conclusion helps spot and correct any mistakes and arrive at an accurate answer @xcite . motivated by such a metacognitive critical thinking skill of humans, there have been various studies to supervise llms to develop self-reevaluation ability where they question and correct themselves @xcite @xcite @xcite @xcite @xcite . on the other hand, recent reasoning models show the ability to reflect, backtrack, and self-validate their reasoning even without explicit training to do so and demonstrate unprecedented reasoning ability @xcite @xcite . therefore, their self-reevaluation skill has begun to gain more focused attention from the research community @xcite @xcite @xcite . this ability is thought to be the key to the impressive success of reasoning models and referred to as a "meta-cognitive" behavior @xcite @xcite . however, it has not yet been systematically studied how effectively they can reevaluate their thinking process and whether it can be regarded as a general "meta-cognitive" ability. our work answers this question by testing how well the models identify and recover from unhelpful thoughts. ## evaluation framework we view the output of reasoning models for a given user input as consisting of two parts: a thinking process and a response. in the thinking process, models generate long chains of thought while reflecting, backtracking, and self-validating their reasoning. the thinking process is encapsulated within a pair of special tokens (e.g., <think>. . .</@xmath0, where the latter is the end-of-thought token generated when they arrive at a final answer. then, models generate a response with the final answer. we use the term thought to denote a chunk of text capturing either a full or part of a thinking process. we assess self-reevaluation in reasoning models through two evaluations. first, we provide models with a reasoning problem and an unhelpful thought, and test how well they can identify the thought as unhelpful in solving the given problem ( §4). second, we evaluate how well models recover from unhelpful thoughts when these are injected into their thinking process. notably, thoughts are injected without an end-of-thought token to allow models to continue their own reasoning. we measure model reevaluation by comparing its task performance with and without thought injection ( §5). ## implications for robustness against jailbreak attacks in §5, we have observed non/inverse-scaling trends for short irrelevant thought injection across differ-ent evaluation datasets and model families. this implies that larger reasoning models are less robust against the injection of short irrelevant thoughts that deviate significantly from their output distribution. this suggests potential brittleness of larger models against irrelevant thought injection attack, which is concerning since larger models often have more capacity to learn and memorize dangerous knowledge. therefore, we perform a simple jailbreak experiment to check whether the non/inversescaling trend transfers to such a scenario. ## conclusion we investigate how reasoning models identify and recover from thoughts that are unhelpful in solving given reasoning questions. while models accurately identify unhelpful thoughts when explicitly asked, they struggle to recover from the same thoughts when these are injected into their thinking processes. we observe non/inverse-scaling trends where larger models get more easily distracted and fail to solve the given questions when shorter irrelevant thoughts are injected in their thinking process. the same trend is observed even when models are provided with explicit cues to recover from the unhelpful thoughts. our jailbreak experiments further demonstrate that the observed trend transfers to a jailbreak scenario; the smallest models from three model families show the best attack robustness against harmful irrelevant thought injection. these findings highlight that current reasoning models have substantial room for improvement in their "meta-cognitive" awareness and recovery from misleading reasoning paths, which is a crucial consideration for developing safer and more reliable large reasoning models.