aiqbench analysis
This research identifies "errors in learning" and Transformer inductive biases as the root causes of looping behavior in reasoning models, rather than simple sampling artifacts. The study demonstrates that while increasing temperature can temporarily suppress loops by encouraging exploration, it fails to address the underlying training distribution mismatches that cause models to get stuck on easy cyclic actions. Ultimately, the paper argues for targeted training-time interventions to resolve these fundamental learning issues instead of relying on inference-time stopgaps.
Wait, Wait, Wait... Why Do Reasoning Models Loop?
arXiv:2512.12895v2 Announce Type: replace Abstract: Reasoning models (e.g., DeepSeek-R1) generate long chains of thought to solve harder problems, but they often loop, repeating the same text at low temperatures or with greedy decoding. We study why this happens and what role temperature plays. With open reasoning models, we find that looping is common at low temperature. Larger models tend to loop less, and distilled students loop significantly even when their teachers rarely do. This points to mismatches between the training distribution and the learned model, which we refer to as errors in learning, as a key cause. To understand how such errors cause loops, we introduce a synthetic graph reasoning task and demonstrate two mechanisms. First, risk aversion caused by hardness of learning: when the correct progress-making action is hard to learn but an easy cyclic action is available, the model puts relatively more probability on the cyclic action and gets stuck. Second, even when there is no hardness, Transformers show an inductive bias toward temporally correlated errors, so the same few actions keep being chosen and loops appear. Higher temperature reduces looping by promoting exploration, but it does not fix the errors in learning, so generations remain much longer than necessary at high temperature; in this sense, temperature is a stopgap rather than a holistic solution. We end with a discussion of training-time interventions aimed at directly reducing errors in learning.
Title:Wait, Wait, Wait... Why Do Reasoning Models Loop?
Abstract:Reasoning models (e.g., DeepSeek-R1) generate long chains of thought to solve harder problems, but they often loop, repeating the same text at low temperatures or with greedy decoding. We study why this happens and what role temperature plays. With open reasoning models, we find that looping is common at low temperature. Larger models tend to loop less, and distilled students loop significantly even when their teachers rarely do. This points to mismatches between the training distribution and the learned model, which we refer to as errors in learning, as a key cause. To understand how such errors cause loops, we introduce a synthetic graph reasoning task and demonstrate two mechanisms. First, risk aversion caused by hardness of learning: when the correct progress-making action is hard to learn but an easy cyclic action is available, the model puts relatively more probability on the cyclic action and gets stuck. Second, even when there is no hardness, Transformers show an inductive bias toward temporally correlated errors, so the same few actions keep being chosen and loops appear. Higher temperature reduces looping by promoting exploration, but it does not fix the errors in learning, so generations remain much longer than necessary at high temperature; in this sense, temperature is a stopgap rather than a holistic solution. We end with a discussion of training-time interventions aimed at directly reducing errors in learning.
Published as Spotlight at ICML 2026. Code: this https URL
Machine Learning (cs.LG)
arXiv:2512.12895 [cs.LG]
(or arXiv:2512.12895v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2512.12895 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 337 words
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