Overview
The ability of LLMs to engage in logical, systematic thinking processes.
Reasoning includes capabilities like breaking down complex problems into steps, backtracking, self-testing, applying abstract principles to new situations, and maintaining a coherent chain-of-thought (CoT) across multiple reasoning stages.
Reasoning enables better problem-solving in complex domains that require multi-step thinking, such as software engineering, strategic planning, or educational tutoring.
It allows models to catch and correct their own errors, engage in self-reflection, and provide more nuanced responses that consider multiple perspectives or potential outcomes.
This is essential for high-stakes applications like legal analysis, business intelligence or scientific research where understanding the ‘why’ behind LLM outputs is just as important as the answers themselves.
Why it Matters
Reasoning is an emergent property of reinforcement learning.
In other words, the skills of long-form thinking, planning, reflecting, and backtracking arise with just the blunt reward of solving a problem. No forcing function required: this is the ah-ha moment of reasoning models.
In RL training runs, models are tasked with solving hundreds of thousands of mathematical, logic, and code tasks. These tasks share a commonality: they can be easily validated.
These binary outcomes provide the sole reward signal required to drive the emergence of reasoning. This is called Reinforcement Learning from Verifiable Rewards (RLVR).
The use of AI judges further unlocks a wealth of scalable validation beyond these binary tasks. LLMs are compilers and verifiers of language: they can check for adherence to instructions, and provide feedback on natural language.
Thus, we can use other LLMs as an additional reward signal for training, expanding the scope of reasoning beyond programming and mathematics.
