What are Large Reasoning Models (LRMs)?

Large reasoning models (LRMs) are artificial intelligence (AI) models that use natural language processing (NLP) combined with reasoning capabilities. They are trained to apply structured reasoning techniques when responding to prompts. LRMs can be used for text, images, and structured data.

LRMs are built using a similar architecture to LLMs, but are trained differently. This is so they can learn reasoning capabilities. Large reasoning models analyze complex prompts to solve a problem step-by-step. They use various information in their training data and apply logic when generating outputs.

Reasoning capabilities enable models to generate outputs that are aligned with real-world scenarios. Models use complex datasets to infer meaning and draw conclusions. This makes LRMs suitable for scenarios where dynamic problem-solving and nuanced decision-making are required, such as medical diagnostics or fraud detection in financial services.

Large language models vs. large reasoning models 

Large language models (LLMs) and large reasoning models (LRMs) share foundational technologies but serve different purposes. Here’s how they compare:

How large reasoning models (LRMs) work

Large reasoning models (LRMs) use a combination of training methods and prompt strategies to enhance the reasoning capabilities of large language models (LLMs). Here’s how they work, step by step:

Training on enriched datasets

LRMs are trained on datasets that include not just language patterns but also examples designed to teach reasoning — such as real-world scenarios with clear outcomes. This helps the model learn both the correct outputs and the reasoning steps needed to reach them.

Reinforcement learning (RL)

Some LRMs are further trained using reinforcement learning — a technique where the model is rewarded for correct or logically consistent answers and penalized for incorrect ones. This helps reinforce desirable reasoning patterns over time.

Human feedback (RLHF)

In certain cases, reinforcement learning from human feedback (RLHF) is applied. This hybrid approach uses human reviewers to guide and refine the model’s outputs, helping it learn nuanced reasoning strategies that align with domain expertise — especially valuable in fields like healthcare and finance.

Prompt engineering

Even after training, how a model is prompted plays a critical role in activating its reasoning capabilities. Effective prompts help guide the model through multi-step tasks or layered questions. Generic prompts may not trigger reasoning behavior, especially for complex challenges.

Chain-of-thought (CoT) prompting

CoT prompting is a method that explicitly encourages the model to break a problem into smaller steps and explore multiple possible reasoning paths. It helps the model select the most logical and consistent approach. This technique enhances transparency and accuracy — essential in enterprise settings such as financial forecasting or clinical decision support, where auditability and explainability matter.

Types of reasoning in large reasoning models

There are four main types of reasoning commonly used in large reasoning models (LRMs), which refer to advanced AI systems — such as large language models (LLMs) — designed to simulate or support human-like reasoning.

Deductive reasoning

Deductive reasoning applies general rules to specific cases to reach logically certain conclusions. It is best suited for tasks that require strict adherence to established rules or facts — also known as top-down reasoning.

Models leveraging deductive reasoning excel at structured, rule-based tasks, as they prioritize logical consistency in generating outputs. This makes them valuable in high-accuracy domains, such as regulatory compliance checks or medical protocol analysis.

Inductive reasoning

Inductive reasoning draws general conclusions from specific observations in training data. LRMs identify patterns and trends to generalize across new, unseen inputs.

Inductive reasoning is less rigid than deductive reasoning and supports probabilistic predictions rather than guaranteed outcomes. It is especially useful in dynamic, data-rich scenarios — such as fraud detection — though it may occasionally yield less reliable results due to its reliance on inference over certainty.

Abductive reasoning

Abductive reasoning involves inferring the most likely explanation based on available evidence. It is particularly useful when data is incomplete or uncertain.

LRMs using abductive reasoning generate hypotheses rather than definitive answers, offering contextually relevant but potentially less precise outputs. This approach is effective in domains like medical diagnostics, where timely, plausible interpretations of ambiguous symptoms are often needed.

Analogical reasoning

Analogical reasoning involves identifying similarities between different situations or datasets and applying insights from one context to another. LRMs trained in this style recognize relational patterns across examples and transfer learned associations to novel inputs.

For example, in retail, an LRM might recommend products to a customer based on purchasing behavior observed in similar customer segments. This type of reasoning supports personalization and contextual adaptation, even when direct rules are not present.