New study uncovers limitations of reasoning models
Created on October 31|Last edited on October 31
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The paper investigates whether modern large reasoning models (LRMs) truly generalize to complex problems, or if their apparent success on benchmarks is a result of limited test complexity. The authors argue that many reasoning benchmarks only cover shallow or modestly complex problems, meaning high scores may not reflect genuine reasoning ability.
How the authors test the models
The authors evaluate both large language models and large reasoning models on tasks where complexity can be gradually increased. They use two categories of problems: symbolic graph connectivity and natural-language proof planning. These tasks are controlled by two variables: lookahead (L), which measures how many steps a model must reason forward, and branching factor (B), which measures how many options appear at each step. By increasing these parameters, they observe how quickly each model’s accuracy collapses.
DeepRD
To enable these tests, they create a dataset called the Deep Reasoning Dataset (DeepRD). DeepRD is both a data generator and a collection of over two thousand examples that can be converted into either graph or language tasks. It allows precise control of complexity through L and B, making it possible to see exactly where reasoning models fail. The dataset is designed to reveal reasoning limits, serving as a lower bound on what models can handle before breaking down.
What the experiments show
The results reveal that models perform very well on existing benchmarks that have low lookahead and small branching factors, but their accuracy collapses sharply when complexity increases. Even strong reasoning models maintain accuracy only for relatively shallow problems. As L and B grow, accuracy drops rapidly, forming steep “cliffs” where performance falls to near zero.
Why token limits aren’t the issue
The authors confirm that these failures are not due to context or token length limits. Models do not reach their token cap, and even when problem chains are linear with no branching, performance still decreases as depth grows. This demonstrates that the weakness is in generalization and logical consistency, not in memory or sequence length.
What this means for real data
When comparing these results to real-world datasets like ConceptNet and NaturalProofs, the authors find that most everyday reasoning problems sit within the models’ comfort zone. However, the long tail of real-world problems often exceeds the models’ reasoning capacity. Proof planning tasks, which require long and branching reasoning, expose these failures more quickly than graph tasks.
Where models go wrong
By examining model reasoning traces, the researchers identify common error patterns. Models often either miss a valid connection (failing to explore the right path) or invent an invalid one (reasoning down a false path). Once an early mistake occurs, the reasoning remains coherent but leads to the wrong conclusion, showing how local correctness can hide global failure.
Why it matters
The key insight is that reasoning models today are shallow generalizers. They excel when complexity is limited but do not maintain accuracy as reasoning depth and branching grow. This limitation means that while models perform impressively on benchmarks and simple queries, they may not be reliable for complex decision-making in fields like law, science, or mathematics. The paper calls for new training and evaluation approaches that explicitly test reasoning depth and robustness.
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