A large reasoning model (LRM) is a language model optimized to solve problems that require multiple steps. It may receive reasoning-focused training, use extra computation while answering, or combine both. The term is descriptive rather than a standardized technical category, so it does not guarantee a particular size, architecture, visible reasoning trace, or level of accuracy.
What does “large reasoning model” mean?
In common usage, an LRM is a large language model fine-tuned or otherwise optimized for multi-step problem solving. IBM describes reasoning models, also called thinking models or LRMs, in these terms: models tuned to work through complex problems. The label points to an emphasis on reasoning tasks, not to a universally agreed model design.
The name overlaps with “reasoning language model.” The authors of Reasoning Language Models: A Blueprint explain their preference for that term: “We use the term ‘Reasoning Language Model’ instead of ‘Large Reasoning Model’ because the latter implies that such models are always large.” The terminology is still evolving, and neither label alone specifies how a system was built.
How do reasoning-focused models work?
Research describes two broad, complementary ways to improve multi-step problem solving: shaping how a model behaves during training and giving it more computation when it is answering. These are approaches, not requirements every LRM must meet.
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Training and post-training
Reinforcement learning and other post-training methods can encourage a model to produce higher-quality reasoning trajectories. This changes the model’s learned behavior; it does not necessarily mean the model uses a distinct architecture. Surveys of reasoning language models discuss such training approaches alongside inference-time methods (survey of reasoning language models; survey of reasoning models).
More computation while answering
A model can also use additional computation at inference time—for example, exploring or refining candidate solution paths—rather than relying only on the scale of pretraining. How much extra computation is used, and whether it can be adjusted, depends on the particular system. The category name does not establish a specific control, response time, or token cost.
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What tasks are LRMs designed for?
Reasoning-focused language-model research targets problems that require connected steps, including mathematics, science, and engineering. A model’s performance should be judged on the specific task and evaluation, not inferred from the LRM label. When comparing named systems, useful evidence includes the benchmark and task, training or post-training methods, inference-time compute controls, latency or token costs, tool availability, and whether reasoning traces are shown.
Does an LRM show its reasoning?
Not necessarily. Some systems expose intermediate text; others may keep intermediate computation internal or reveal it selectively. A displayed reasoning trace is an intermediate output, not automatically a faithful explanation of what caused the final answer. The term LRM does not promise that a complete or reliable chain of thought will be visible.
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What the label does—and does not—tell you
- It suggests: the model is intended or optimized for multi-step problem solving.
- It does not establish: a standard architecture, a particular number of parameters, or a minimum model size.
- It does not guarantee: correctness, reliability across tasks, or a visible explanation that faithfully represents internal processing.
- It does not make systems directly comparable: capabilities and costs depend on the model, task, evaluation conditions, and available tools.
What a security study can—and cannot—show
A 2026 Nature Communications study examined autonomous, multi-turn jailbreak attempts involving four LRMs and nine target models. The authors reported an aggregate jailbreak success rate of 97.14% across the evaluated model combinations (Nature Communications study). That result describes the study’s particular experimental setup; it is not a general success rate for LRMs or a measure of what happens in ordinary user interactions.
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