Title: Evaluating the Inductive Abilities of Large Language Models: Why Chain-of-Thought Reasoning Sometimes Hurts More Than Helps
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large Lauange Model

Inductive Abilities

Reasoning
Abstract: Large Language Models (LLMs) have shown remarkable progress across domains, yet their ability to perform inductive reasoning—inferring latent rules from sparse examples—remains limited. 
It is often assumed that chain-of-thought (CoT) prompting, as used in Large Reasoning Models (LRMs), enhances such reasoning. 
We investigate this assumption with creating four controlled, diagnostic game-based tasks—chess, Texas Hold’em, dice games, and blackjack—with hidden human-defined rules. 
We find that CoT reasoning can degrade inductive performance, with LRMs often underperforming their non-reasoning counterparts.

To explain this, we present a theoretical framework that reveals how reasoning steps can amplify error through three failure modes: incorrect sub-task decomposition, incorrect sub-task solving, and incorrect final answer summarization. 
Based on our theoretical and empirical analysis, we introduce structured interventions that adapt CoT generation according to our identified failure types. These interventions improve inductive accuracy without retraining. Our findings suggest that effective (CoT) reasoning depends not only on taking more steps but also on ensuring those steps are well-structured.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper examines the inductive capabilities of LLM by employing four meticulously designed games. The findings reveal that LRM exhibit inferior performance in capturing inductive rules compared to LLM. Furthermore, the introduction of irrelevant or misleading CoT can even negatively impact LRM’s performance. Through human evaluation, the authors identified the primary types of reasoning failure. Consequently, the authors proposed a targeted intervention to effectively address these issues.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n* This is an interesting finding that LRM shows poorer performance compared to LLM  when evaluating the inductive ability through specially designed games\n\n* The authors introduced several temps of reasoning failures through aberration to help identify the issues by decomposing the failures into a few core types. \n\n* The authors proposed solutions to address the issue via targeted intervention. \n\n\nWeakness:\n\n* The authors should provide a bit more examples to motivate audience and justify the value of the work., making it more clear that the issue exists, not due to some specific designed games or data patterns. \n\n* The authors should provide a bit more details, such as a  concrete example, especially about the specifically designed games that help evaluate the performance of the models, so that audience can better understand  the assessment processes, and therefore understand  how the inductive ability is quantitatively measured when reading Figure 2 in Section 3.3 .\n\n* It is better having a table of the notations. In Theorem  4.1,  the minimiser N* was used without proper definition, which can cause some confusion or extra efforts to tell.\n\n* The experimental analysis can be enhanced to show more insights of the proposed techniques.\n\nQUESTIONS:\nn/a\n\nLIMITATIONS:\nn/a", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper first proposed four controlled game-based tasks that contains both standard gameplay conventions and newly defined constraints. Then, it shows that chain-of-thought (CoT) prompting can hurt the inductive performance of reasoning models through these tasks and presents a theoretical framework that explains how reasoning steps amplify errors through different types of CoT steps. The paper also introduces interventions that can improve inductive accuracy.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n- The paper embeds hidden self-defined rules in addition to the normal rules in common games, and finds that reasoning models often underperform non-reasoning models on inferring the hidden rules is interesting. This directly contradicts the common belief that \"more reasoning = better performance.”\n- The paper provides a theoretical framework that decompose chain-of-thought reasoning into different operations. They also defines different reasoning errors:incorrect sub-task decomposition, incorrect sub-task solving, and incorrect final answer summarization, using the theoretical framework.\n\n## Weaknesses\n\n- This paper heavily rely on GPT-4o as a judge, potentially introducing bias towards certain types/answer styles. Even small scale of human evaluation can help.\n- The paper has a hypothesis of “the base model has fewer CoT while the reasoning model has more CoT“. However, equation 5 states that either under- or over- reasoning can incur this summary error. Section 4.3 empirically classified such errors, but only for the reasoning models. No analysis of base models' reasoning processes and errors. The hypothesis is stated but doesn’t seem to be proved?\n- There’s no comparison against other reasoning method. A proper comparison could be GPT-4o (or non-reasoning models) + explicit CoT instructions, or GPT-4o + tree-of-thoughts. These setting might match the claims better, as the base model remains the same, only the amount or style of reasoning changes.\n\nQUESTIONS:\n- I noticed models like GPT-4o also generates chain-of-thoughts reasoning when using them. Why is “their multi-step traces may not help” for inferring the hidden rules, rather than “the procedure of training the reasoning models may not help”? Maybe it is harder for them to adapt the models to new rules because they were heavily trained to stick on the existing rules? Is it possible that for popular games like blackjack or Texas Hold’em, the reasoning model are trained on them heavily by RL, therefore it is harder for them to adapt to new rules?\n- In equation 3 the $\\gamma_k$ is in (0, 1), given the presence of self-reflections/corrections in these reasoning models, should the $\\gamma_k$ be in (-1,1) instead?\n- In figure 3, where does it reflect the improvements of summarization as indicated in line 332-333? The color is not that easy to tell apart. The only blue part that I see are in Chess for NR4 GPT-4o, but on top of the summarization blue it is still GPT-4o colored blue? I am very confused. Please improve the visual a bit.\n- I don’t think the word “counterparts” is appropriate when contrasting the performance between models like o3 and GPT-4o. Calling them \"counterparts\" implies they're the same model with/without reasoning, since there’s no public evidence that one equivalently trained / based-on another like R1 vs V3.\n- In appendix K, the anonymous link only contains a README file.\n\nLIMITATIONS:\nNo limitations are addressed. Please include the limitations of the problem setup (four game-based tasks), the evaluation bias (only uses GPT-4o), and the hypothesis.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper investigates the effect of chain-of-thought reasoning on the inductive abilities of LLMs. Using games-based tasks with some hidden rules, the paper observes that reasoning based LLMs often perform worse than their non-reasoning counterparts, particularly on tasks requiring the inference of non-obvious or special hidden rules. The paper then presents a theoretical framework that points to three primary failure modes in the reasoning process: potentially incorrect sub-task decomposition, incorrect sub-task solving, and flaws in final answer summarization. Based on this analysis, the paper proposes and validates a set of structured interventions—such as using decomposition templates and guiding the solving process—that successfully improve the inductive accuracy of LRMs without needing to retrain the models. The central argument is that the quality of inductive reasoning in LLMs hinges not merely on performing more reasoning steps, but on the criteria that those steps are well-structured and accurate.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is a fairly rigorous paper and the tasks used provide a nice and controlled environment to test inductive reasoning.  The core empirical finding—that LRMs with explicit CoT reasoning consistently underperform non-reasoning LLMs on special rules—is a significant and I think a fairly non-obvious result (based on the general acceptance of these). The theoretical framework is clear and can help in understanding when it will fail reasoning. The decomposition of reasoning errors into three distinct types is also intuitive and powerful. Further, the empirical validation makes it strong. The interventions seem to be directly derived from failure modes. \n\nOn weaknesses: I feel the title is overtly attention grabbing and seems like just reading the title alone oversimplifies the findings of the paper. The paper itself shows that it is specifically poorly structured or unconstrained reasoning that is detrimental, and that well-guided reasoning is highly beneficial. The claim could be more precisely worded to reflect that current CoT mechanisms can be flawed, rather than suggesting reasoning itself is the problem. \nThere are some rigour issues: The classification of reasoning failures was conducted by two human annotators. While the paper states that disagreements were resolved collaboratively, this process is inherently subjective. Without quantitative metrics like inter-annotator agreement scores, the reliability of the error distribution presented in Table 1 is hard to independently verify.  The empirical analysis of failure modes was also performed on three specific LRMs. Although the findings are consistent across these models, it is a significant leap to generalize these specific failure patterns to all current and future reasoning models. This requires a careful discussion about the limitations, at the very least. The primary metric for success—rule-level accuracy—is determined by using GPT-4o as a judge to assess semantic alignment — that’s circular and off. There’s not a lot of careful analyses here.\n\nQUESTIONS:\nThe paper fairly convincingly shows that they are better at inducing special rules, but not why.  Are they using a more robust, implicit form of reasoning, or are they relying on a different form of pattern matching that is less prone to the error amplification seen in multi-step CoT?\n\n\nThe interventions presented in the paper are fair in the games-setup, but do they generalize to other domains at all? \n\nCould the identified failure modes be addressed through targeted fine-tuning instead of prompting?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the performance of Large Language Models (LLMs) in inductive reasoning tasks and finds that the widely used Chain-of-Thought (CoT) reasoning approach is not always beneficial, but instead may degrade model performance when reasoning about hidden rules. The authors designed four game-like tasks (Chess, Texas Hold'em, Dice Game, and Black Jack) containing hidden human rules, systematically evaluated the inductive power of eight mainstream language models, and found that inference-enabled models are often inferior to models without inference when dealing with complex, abstract rules. To explain this phenomenon, the authors propose a theoretical framework that identifies three major failure modes in the reasoning process: incorrect subtask splitting, incorrect subtask solving, and inappropriate answer summarization, which gradually amplify errors in the reasoning chain. Based on the theoretical analysis, the authors further propose three interventions that do not require retraining and significantly improve the model's accuracy in summarization tasks by structurally guiding the reasoning process. This study challenges the common assumption that “more reasoning is better” and emphasizes that well-structured, goal-oriented reasoning is more critical than blindly extending the reasoning chain.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The paper addresses a meaningful and timely problem—whether reasoning via chain-of-thought (CoT) actually helps inductive tasks—and supports it with well-designed, controlled experiments using four diverse game-based benchmarks.\n- It provides a rigorous theoretical framework that identifies and formalizes three distinct failure modes in reasoning, supported by clear mathematical derivations explaining performance degradation.\n- The proposed prompting interventions—structured decomposition, guided solving, and output length control—are simple yet effective, yielding significant accuracy gains without retraining.\n\nWeaknesses\n- The comparison between reasoning and non-reasoning models is potentially confounded by version and size mismatches. For example, DeepSeek-V3 uses the latest version from March 2025, while DeepSeek-R1 is based on an older January 2025 version and derived from an earlier DeepSeek-V3. Differences in model sizes between reasoning and non-reasoning variants may also bias the conclusions and require further clarification.\n- The theoretical framework, while well-formalized, lacks intuitive illustrations or concrete examples. Readers may find it hard to understand the roles of abstract variables like αₖ (alignment) and εₖ (noise) without visual aids or case walkthroughs.\n- The paper lacks ablation studies to isolate the effect of each intervention. Such experiments would help confirm the effectiveness of each strategy and validate their correspondence to the identified failure modes.\n\nQUESTIONS:\nQ1. The comparison between reasoning and non-reasoning models may be confounded by differences in model versions and sizes. Further clarification is needed to ensure fairness and validity of the comparisons.\n\nQ2. While the theoretical framework is well-formalized, it lacks intuitive illustrations or concrete examples that would aid comprehension. Including visual aids or specific cases could enhance the clarity and persuasiveness of the theoretical analysis.\n\nQ3. The study does not include ablation experiments to isolate the individual effects of each proposed intervention. Adding such analyses would help verify the contribution of each intervention to the overall performance improvement.\n\nQ4. Could these findings potentially be extended to more common reasoning problems beyond the specific cases studied?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-07"}