large reasoning model (LRM)

A large reasoning model (LRM) is a language model optimized for multi-step problem-solving that allocates extra computation and uses structured intermediate steps during inference to plan, verify, and refine its answers.

LRMs extend standard LLMs with training and inference techniques, including some of the following:

  • Reinforcement learning on reasoning traces
  • Explicit reasoning token for test-time thinking
  • Search or self-consistency to improve correctness

Many LRMs also use external tools, such as code execution, to enhance their reasoning.

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Build type-safe LLM agents in Python with Pydantic AI using structured outputs, function calling, and dependency injection.

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By Leodanis Pozo Ramos • Updated July 5, 2026
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