Title: Reasoning Models Better Express Their Confidence
Venue: NeurIPS 2025
Accepted: yes
Keywords: Reasoning Models

Confidence Estimation

Calibration

Large Language Models
Abstract: Despite their strengths, large language models (LLMs) often fail to communicate their confidence accurately, making it difficult to assess when they might be wrong and limiting their reliability. In this work, we demonstrate that reasoning models that engage in extended chain-of-thought (CoT) reasoning exhibit superior performance not only in problem-solving but also in accurately expressing their confidence.
Specifically, we benchmark six reasoning models across six datasets and find that they achieve strictly better confidence calibration than their non-reasoning counterparts in 33 out of the 36 settings. Our detailed analysis reveals that these gains in calibration stem from the slow thinking behaviors of reasoning models (e.g., exploring alternative approaches and backtracking) which enable them to adjust their confidence dynamically throughout their CoT, making it progressively more accurate. In particular, we find that reasoning models become increasingly better calibrated as their CoT unfolds, a trend not observed in non-reasoning models. Moreover, removing slow thinking behaviors from the CoT leads to a significant drop in calibration. Lastly, we show that non-reasoning models also demonstrate enhanced calibration when simply guided to slow think via in-context learning, fully isolating slow thinking as the source of the calibration gains.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the confidence calibration of reasoning and non-reasoning language models and finds that reasoning models consistently demonstrate better confidence calibration across a variety of datasets.\nFurther analysis shows that improvement stems from exploring alternatives and backtracking.\nBased on this finding, this work propose a simple but effect few-shot method to calibrate the non-reasoning model.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengthness\n1. The observation that reasoning models express confidence more effectively is  novel. The analysis provided is intuitive and has the potential to influence future developments in both confidence calibration and reasoning-model.\n2. The analysis in Section 4.2 carefully disentangles multiple factors within the reasoning process that may affect calibration, ultimately identifying _non-linear reasoning_ as the dominant contributor. This insight is valuable and directly informs the design of the simple but highly effective calibration method proposed in Section 4.3.\n\n\n### Weakness\n\n1. Prompt Is Very Important! The same prompt is used for both reasoning and non-reasoning models, it appears to be specifically designed for reasoning models. [1] shows prompting non-reasoning models to output CoT or express confidence linguistically can actually degrade calibration. It is possible that the observed calibration advantage of reasoning models is prompt-specific. The authors should consider testing multiple prompt variants for non-reasoning models, including the most effective ones reported in [1].\n2. The calibration gap are relatively small in some cases. For instance, in Table 10 (TriviaQA), the Brier score difference is only 0.005 and AUROC differs by 0.015. These are modest gains and may fall within the range of variation caused by prompt sensitivity alone.\n\n\\[1\\] Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback\n\nQUESTIONS:\n1. TriviaQA is a free-form QA dataset. How is answer correctness determined in this paper? Is it based on exact match, or model-based scoring? The method used to assess correctness should be clearly specified\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-14"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work presents a study showing how reasoning models are better calibrated. The study is conducted on multiple LLMs and four different datasets. When explicitly asked to reason about confidence in their thoughts, the reasoning models show better calibration ability according to three metrics (ECE, Brier, and AUROC) despite having similar accuracy. This study also contains ablations showing what aspects of the thoughts are helpful, and shows non-reasoning models can also demonstrate similar improvements by using the thoughts as in-context examples.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n1. Understanding how reasoning models (or thinking models) change their behaviors compared to other models is an important topic.\n2. I like the ablations studying what specific parts of the thought help calibration. The analysis is well-designed and insightful.\n3. The experiment is comprehensive, covering multiple models and multiple datasets.\n\n## Weaknesses\n1. I'm fairly convinced that the results in this study are generalizable, but it would still be great to show multiple ways of confidence expression in this work. Currently, this work only shows results on verbalized confidence. It would be interesting to see if the same trends also hold if you sample the models multiple times and compute the confidence from samples. Additionally, I think with greedy decoding, it's probably very hard to sample low-confidence answers, and that's why in Figure 2, the lower part of that figure has very few samples.\n2. This is a minor point. I'm slightly concerned about using \"Wait\" as the token to enforce longer CoT in Line 299. I don't feel \"Wait\" is semantically neutral and it can lead to unintended changes, hence hurting calibration. It would be better if the same study were implemented by just avoiding the tokens that end the thinking process.\n\nQUESTIONS:\n1. How do you design the prompts that are used in this study? Have you tried multiple different prompts? Is the model sensitive to these changes?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies reasoning models' behavior on confidence and find that they not only perform better but also give more reliable confidence. They test six models on six tasks and see that reasoning models are better at expressing their confidence in almost all cases. The authors further identified that this is because reasoning models take time to think and try different ideas, which helps them adjust their confidence. This paper finally pointed out that the confidence gets worse for reasoning models without slow thinking, while non-reasoning models can improve confidence if they are guided to think step-by-step.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. Exploring confidence of reasoning models is an important problem.\n\n2. Comprehensive experiments and ablations to obtain the conclusion.\n\n3. Writing is good and clear.\n\nWeakness:\n\n1. The finding that increased reasoning (CoT) correlates with higher confidence is not novel to this paper. Extensive prior research has established that CoT typically leads to enhanced confidence [1-3].\n\n[1]. Wang, Xuezhi, and Denny Zhou. \"Chain-of-thought reasoning without prompting.\" arXiv preprint arXiv:2402.10200 (2024).\n[2]. Wang, Zezhong, et al. \"Chain-of-Probe: Examing the Necessity and Accuracy of CoT Step-by-Step.\" arXiv preprint arXiv:2406.16144 (2024).\n[3]. Foodeei, Darius, Simin Fan, and Martin Jaggi. \"Semantic uncertainty in advanced decoding methods for LLM generation.\" arXiv preprint arXiv:2506.17296 (2025).\n\n2. Examining solely the relationship between reasoning and confidence may be superficial. The connection between improved confidence and enhanced correctness remains unclear. Previous studies [4-5] have suggested that while long CoT reasoning can boost confidence, it may simultaneously reinforce biases and hallucinations, potentially leading to incorrect conclusions.\n\n[4]. Lu, Haolang, et al. \"Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Models.\" arXiv preprint arXiv:2505.13143 (2025).\n[5]. Cheng, Jiahao, et al. \"Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation.\" arXiv preprint arXiv:2506.17088 (2025).\n\n3. Reasoning models may perform significantly worse than non-reasoning models on some knowledge-dense benchmarks such as SimpleQA. It would be interesting to investigate that how reasoning could impact confidence and correctness on these benchmarks.\n\n4. It would be beneficial to conduct more controlled training experiments to establish robust conclusions. For instance, since QwQ and R1-Distill-32B underwent post-training on large-scale and diverse datasets, directly comparing them with Qwen2.5-32B-Instruct may not constitute a fair experimental setup.\n\nQUESTIONS:\nPlease see Weakness 2-4.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 1, "ORIGINALITY": 1, "DATE": "2025-07-01"}