Most systems that employ reasoning today rely on static reasoning: every input gets the same model, the same prompt and the same depth of reasoning, leading to inefficiency and wasted time and money. A trivial query might get over-processed, driving up cost and latency. A complex, high-stakes task might be underserved, leading to risky errors.
In my view, the next frontier in production-ready reasoning is adaptive reasoning: AI systems that allocate just the right amount of reasoning per input, balancing accuracy, cost and latency in real time. For CIOs, adaptive reasoning may be a new operating model for how enterprise AI systems should be designed, deployed and scaled.
What are reasoning language models (RLMs)?
Reasoning language models are language models that can generate a thinking process. They start with a question, produce reasoning steps and arrive at an answer. RLMs can move beyond simply mapping an input to an output; they can actively engage in a multi-step decision-making process.
