Title: When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large language models

Reasoning

Instruction Following

Chain of Thoughts
Abstract: Reasoning-enhanced large language models (RLLMs), whether explicitly trained for reasoning or prompted via chain-of-thought (CoT), have achieved state-of-the-art performance on many complex reasoning tasks. However, we uncover a surprising and previously overlooked phenomenon: explicit CoT reasoning can significantly degrade instruction-following accuracy. Evaluating 20+ models on two benchmarks: IFEval (with simple, rule-verifiable constraints) and ComplexBench (with complex, compositional constraints), we consistently observe performance drops when CoT prompting is applied. Through large-scale case studies and an attention-based analysis, we identify common patterns where reasoning either helps (e.g., with formatting or lexical precision) or hurts (e.g., by neglecting simple constraints or introducing unnecessary content). We propose a metric, constraint attention, to quantify model focus during generation and show that CoT reasoning often diverts attention away from instruction-relevant tokens. To mitigate these effects, we introduce and evaluate four strategies: in-context learning, self-reflection, self-selective reasoning, and classifier-selective reasoning. Our results demonstrate that selective reasoning strategies, particularly classifier-selective reasoning, can substantially recover lost performance. To our knowledge, this is the first work to systematically expose reasoning-induced failures in instruction-following and offer practical mitigation strategies.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper identifies an interesting phenomenon, language model optimized for reasoning will usually have a surprising degradation on instruction-following ability. The authors analyzed why such failures occur, places where reasoning helps and places where reasoning might hurt.\n\nThe authors did comprehensive experiments on a wide set of models including reasoning and non-reasoning models, with or without CoT, and show that there's a consistent trend of instruction-following performance degradation across almost all models. In addition, the authors proposed several methods to help mitigate such failures, and showed that a classifier-selection based method can help bring back the ability on instruction-following.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper is overall well-written.\n- The authors identified an important phenomenon: the model becomes worse at instruction-following when optimized for reasoning, which is surprising and remains to be under-explored in existing literature. The authors also did comprehensive analysis on specific cases showing why the model might exhibit such behavior.\n- Experiments are performed over a wide set of models and on two instruction-following benchmarks. The authors have also proposed four different mitigation methods and showed they yield various gains on the IF benchmarks.\n\nWeaknesses:\n- I wonder how the classifier-selective method generalizes in practice. It does yield good performance on the two benchmarks, but could be due to the classifier is trained to identify similar patterns (since the patterns on the two IF benchmarks are fairly limited), and learn to use or not use CoT on those patterns. Can the authors split the data according to different patterns, train the classifier on a set of those patterns, and test on another set? In practice we would hope such classifier can generalize to a wide set of unseen patterns in order to be useful.\n- Another simple mitigation could be training the model directly for instruction-following. Have the authors considered this (e.g., RL with rewards as the instruction is being followed vs not) and how would it compare with the classifier based method?\n\nQUESTIONS:\nSee above.\n\n- Classifier-selective method: Can the authors split the data according to different patterns, train the classifier on a set of those patterns, and test on another set? In practice we would hope such classifier can generalize to a wide set of unseen patterns in order to be useful.\n- Another simple mitigation could be training the model directly for instruction-following. Have the authors considered this (e.g., RL with rewards as the instruction is being followed vs not) and how would it compare with the classifier based method?\n\nLIMITATIONS:\nDiscussed in Section 6.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper investigates a counterintuitive phenomenon where explicit Chain-of-Thought (CoT) reasoning can impair the ability of Large Language Models (LLMs) to follow instructions.\n\nThe paper uses two setups:\n1) a model is prompted with and without a specialized prompt for the model to provide thinking before the final answer\n2) a model is used with/without its native thinking mode (e.g. claude sonnet with and without thinking)\n\nThe authors show that performance tend to drop when thinking is used in two IF datasets.\n\nMy main concerns are that:\n* setup (1) is not valid imo to assess the reasoning abilities of models, as models are prompted with a specialized instruction to think, which they most likely weren't trained to tackle. The point is that we shouldn't expect this specific prompt to unlock thinking in models.\n* I think setup (2) is much more valid, as it compares a model with its native thinking behavior. My problem here is that results comparing thinking on/off are pretty close, and there is so significance test, so we cannot know whether there is any model degradation.\n\nGiven these weaknesses I think the paper requires more work\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n* The central claim that reasoning can degrade instruction-following accuracy is unexpected and important. \n* In-depth Analysis of Failure Modes: The paper does not just report the phenomenon but offers a deep dive into why it occurs. It provides a qualitative analysis through a large-scale manual case study, identifying recurring patterns where reasoning helps (e.g., formatting, lexical precision) and where it hurts (e.g., neglecting simple constraints, introducing unnecessary content).\n\nWeaknesses (copying first 2 bullet points from above):\n* setup (1) is not valid imo to assess the reasoning abilities of models, as models are prompted with a specialized instruction to think, which they most likely weren't trained to tackle. The point is that we shouldn't expect this specific prompt to unlock thinking in models.\n* I think setup (2) is much more valid, as it compares a model with its native thinking behavior. My problem here is that results comparing thinking on/off are pretty close, and there is so significance test, so we cannot know whether there is any model degradation.\n* Limited Scope of Investigation: The study's conclusion is confined to instruction-following evaluation (formatting and other simple constraints). What about addressing the actual quality of the responses?  \n* I think the results for methods to mitigate degradation (section 5) are not very informative as they are only applied to setup (1), which is problematic as I mentioned above.\n\nQUESTIONS:\n* are the results for table 2 significant (i.e., is the performance when thinking is on significantly worse)?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper examines when chain of thought reasoning in LLMs leads to degrading performance. To this end, they show that explicit chain of thought reasoning can harm instruction following accuracy by systematically evaluating responses with and without chain of thought; applying the method on two benchmarks to a variety of LLMs from different LLM families from 1 to 70 B-parameters shows performance drops in most models. To mitigate the effect, the authors analyze individual reasoning scenarios and introduce an attention-based metric to quantify the influence in these scenarios, such as few-shot in-context learning and self-reflection.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is well written and tackles a highly interesting topic: reasoning and language models, specifically where it might not be helpful. \n\nWhile methodologically the paper just compares explicit chain of thought with no chain of thought, it carries out a thorough comparison and evaluation across a variety of language models and also reasoning scenarios. \n\nIt proposes an interesting approach to analyze attention patterns using attention interpretability methods, and shows illustrative visualizations of the relevant tokens, where the relevant tokens are identified using GPT-4. These relevant tokens are clustered into rules that are derived from the two benchmarks, giving the possibility for an interesting fine-grained analysis. (The statistics derived from this analysis include the attention trace and also layer-wise statistics, showing that chain of thought reasoning flattens the constraint-attention trace.)\n\nFollowing their evaluation it derives concrete best practices for using reasoning in different scenarios, making the approach easily applicable.\n\nWeaknesses:\nMy main concerns are with the generalizability of the approach:\nFirst, the approach focuses only on English it would be interesting to see how it performs in other languages, maybe less resourced languages. \nSecond, I would find the approach more generalizable if it was also evaluated on tasks not specifically designed for reasoning.\n\nThere is (recent, but also older) related work that tackles similar aspects of reasoning, e.g., chain of though length, [1, 2, 3] that would be important to include and discuss here. \n\n[1] https://aclanthology.org/2024.acl-long.818.pdf\n[2] https://arxiv.org/pdf/2502.07266\n[3] https://arxiv.org/pdf/2410.21333\n[4] https://arxiv.org/pdf/2502.01100\n\nQUESTIONS:\nWhat other methods from mechanistic interpretability (MI) could be used to analyze attention traces or relevant parameters and the im MI so-called residual stream during generation? \n\nHow can similar issues arisong from (mis-) using chain of thought patterns be mitigated by combining different strategies, such as self-reflection or reasoning in probability space, or constrained reasoning?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the performance large language and reasoning models with respect to instruction following under chain-of-thought prompting. Experiments have been conducted with different models (both open and proprietary) of varying capabilities. The authors find that instruction following degrades under COT conditions for almost all models on both benchmarks. They then apply several mitigation methods to attempt to restore instruction following capabilities. The paper also proposes and explores constraint attention to examine the degradation of model performance for instruction following.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- Many work pipelines require LLMs to adhere to instructions under COT, so it is important to study this topic. \n\n- This paper runs experiments on a wide range of models which many different levels of capability.\n\n- The paper makes some interesting new observations with regard to COT prompting for LLMs. They also provide several different methods for fixing these instruction following issues.\n\n- The paper additionally proposed constraint attention and showed how it can be used to examine failure cases for instruction following.\n\n- Overall, the paper is well-motivated and the flow of analysis and proposed methods is clear.\n\nWeaknesses\n\n- While the initial findings of COT harming instruction following is interesting, the follow-up mitigation methods are quite straightforward. Also, CoT may work due to different reasons. Investigation based the existing results (e.g., [1]) could help provide more insights. \n\n- I believe there is a missing citation [2], on the effects of output constraints (e.g. word count, formatting) and context adherence (i.e. faithfulness to RAG facts) as two separate entities. In this paper, the authors collate the two and contrast them with CoT reasoning. It would strengthen the paper to add commentary on the distinction between these two types of instructions (output constraints vs. context adherence), especially since they observe in line 166-171 that CoT reasoning enhances some output constraints.\n\n- There are no statistics to support the trends in lines 165-182 - some basic values such as average % increase in correct formatting, average % new errors in word count, etc. I believe this would improve the clarity/transparency of their observed trends. Since the authors point these out specifically, it would also help to understand if the mitigation strategies effectively reverse these trends, or if they are improving instruction following elsewhere.\n\n- For method 4, lines 283-285, the authors mention they use a 50-50 split to train an external classifier. It is unclear if the 50-50 split is consistent across all experiments - if not, that is a significant drop in test samples (~750 compared to ~1500) and it is hard to say the results on Method 4 are comparable to the other experiments.\n\n- Figure 1 and 2 use a WIN/LOSE metric to split CoT generations and there is some score metrics, but I did not see an explanation for the score.\n\n- Overall, I would have liked more discussion/analysis about the individual models tested, even at the cost of fewer mitigation methods reported. While the thesis is clear, the analysis is too high-level considering the number of models tested.\n\n[1] https://arxiv.org/pdf/2304.03843\n\n[2] Wu, Z., Zhang, Y., Qi, P., Xu, Y., Han, R., Zhang, Y., Chen, J., Min, B., & Huang, Z. (2024). Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models. Conference on Empirical Methods in Natural Language Processing. https://arxiv.org/pdf/2407.21417\n\nQUESTIONS:\n-\tIs the decreased performance in instruction following caused by the reasoning aspects of COT/reasoning models, or is it simply a byproduct of the increasing sequence length?\n-\tTo follow up on that, in Appendix I, this question is partially answered by showing the length of the reasoning for the COT response is NOT strongly correlated to the difference in score between the COT and base models. This to me unintuitive, can we see correlations between the length of the tokens and the model accuracy as well as the differences in total token length between the COT and base models. Apologies if I missed this in the paper somewhere.\n-\tWhen COT prompting a reasoning model, do the models exhibit excessive reasoning where the over analysis may lead to failure? What is the difference in token count between the original reasoning model response and the COT reasoning model response and is that correlated with the performance?\n-\tDeepseek distill models had the most benefit from few-shot demonstrations, but their performance was also the weakest. Since Deepseek is a “reasoning” model it seems like they weren’t trained with extensive instruction following capabilities to begin with, and mainly distilled the reasoning ability of DeepSeek-R1. \n-\tI would like to know more about how the manual examination (L155) process was done, in addition to the resulting categories and the insight provided.\n-\tThe choice of using 0 temperature (L128): I would like to see the reason for choosing this temperature.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 2, "DATE": "2025-07-01"}