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LLM repetition can arise from how text is generated, how a model has learned to predict continuations, and repetition in its training data. These are useful places to investigate—not statistically independent causes. They can interact: a decoding choice may expose a learned tendency, and repeated text in the current context can make more repetition likelier. Changing a generation setting may interrupt a loop without correcting what produced the tendency.
How to distinguish the three causes
Start by separating a visible symptom from its possible origins. A loop during generation points first to decoding and the current context. A model that repeatedly favors certain tokens or actions may reflect learned behavior. Repeated examples in training data can also shape what a model learns, though an output alone cannot reveal a model’s training history.
| Place to investigate | What it can explain | What the evidence does not establish |
|---|---|---|
| Decoding and current context | Why a generation can enter or continue a loop. | That changing a decoding setting fixes the model’s underlying learning errors. |
| Learned model tendencies | Why a model may overpredict repeated tokens or favor an easy cyclic action. | That every repetition comes from one shared internal mechanism. |
| Training-data repetition | How duplicated examples can affect model performance in controlled experiments. | That a particular deployed model was trained on repeated data, or that its loops were caused by duplication. |
1. Decoding can keep a repetition loop going
Decoding is the process that selects each next token from the model’s predictions. Greedy and other maximization-based choices can get stuck in sentence-level repetition. Xu et al.’s 2022 NeurIPS study reports that “the more times a sentence is repeated in the context, the higher the probability of continuing to generate that sentence.” In other words, once a sentence has been repeated, it becomes part of the context used for the next prediction, potentially reinforcing the loop. The paper also reports that consecutive sentence-level repetition represented 0.02% of WikiText-103; that figure describes this dataset, not text corpora in general. Read the NeurIPS paper.
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In a 2026 study of open reasoning models, Pipis et al. report that looping is common at low temperature. Raising temperature encourages exploration and reduces looping in their setting, but does not resolve the learning errors behind it. The authors characterize temperature as a stopgap: at high temperature, generations can remain much longer than necessary. This is evidence about the studied reasoning models, not a universal setting recommendation for every LLM or task. Read the ICML paper.
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There is no universal temperature value established by these studies. If you control generation settings, treat temperature as a possible symptom-level mitigation and assess the result for both repetition and unnecessary length.
2. A model can learn to overpredict repetition
Repetition may persist because of the model’s learned predictions, not just the decoding choice made at inference time. In work on open-ended generation, Guan and Huang attribute token-level repetition overestimation to a learning bias: their paper says language models capture simple repetitive patterns faster under maximum-likelihood estimation (MLE) training. This is a proposed explanation in that study, not proof that all repetitive output has the same cause. Read the ACL Findings paper.
Pipis et al. examine a different setting: open reasoning models. They identify mismatches between training distribution and learned behavior as a key cause of looping in their study. Their analysis describes risk aversion when a progress-making action is hard to learn but an easy cyclic action is available, along with temporally correlated errors that can repeatedly select the same actions. These findings concern the models and tasks they studied; they should not be collapsed into the token-level mechanism proposed for open-ended text generation.
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Training interventions are research approaches
Guan and Huang propose self-contrastive training, while Xu et al. propose DITTO as approaches to mitigating repetition. Pipis et al. discuss interventions aimed at learning errors in reasoning models. These are research methods, not established drop-in fixes validated across all current models. The cited sources do not offer a common head-to-head comparison or establish one best intervention.
3. Repeated training data can affect what a model learns
Training-data repetition is a separate avenue to investigate from a loop in one generation. Anthropic’s 2022 study on learning from repeated data reports a double-descent pattern: performance worsened across a range of repetition frequencies. As a concrete result from its experimental setup, Anthropic says that repeating 0.1% of the data 100 times could degrade an 800-million-parameter model to the performance of a 400-million-parameter model, despite the other 90% of training tokens remaining unique. This is a finding from that experiment—not a general threshold, a rule for all models, or evidence about the training history of a particular commercial model. Read Anthropic’s study summary.
This result shows why training-data curation may matter, but it does not diagnose an individual loop. Establishing whether data duplication contributed to a particular model’s behavior would require evidence about that model’s training data and process.
What to check when an LLM repeats itself
- Look at the generation setup. Check the decoding method and temperature, then see whether the repetition is already present in the context or grows as the output continues.
- Check whether the behavior follows the model. If you can compare outputs under different decoding choices, note whether repetition changes. A change may implicate decoding, but it does not rule out a learned tendency.
- Separate symptoms from training explanations. Repeated output alone does not show whether the model overpredicts repetitive patterns or was affected by duplicated training examples.
- Match evidence to the setting. The cited research covers open-ended generation, summarization, open reasoning, and controlled repeated-data experiments. A result in one setting should not automatically be generalized to another.
A diagnosis is strongest when it considers the generation setup, the model’s behavior, and available evidence about training data separately. The studies address different settings and do not provide one common comparison across all three causes.
One terminology distinction
Unwanted repetition loops are not the same as the “repeated token” phenomenon studied by Yona et al. In that distinct problem, a model may fail when asked to repeat a single word and instead produce unrelated text. Their 2025 study connects the behavior to attention sinks and reports a targeted patch. It concerns failure to comply with a repetition request, not a model generating an unwanted loop. Read the ICML paper.
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