How to Control LLM Reasoning Effort
Ahead of AI (Sebastian Raschka) · rss · 2026-07-18
This article explores how to control an LLM's reasoning effort, reviewing the basic concepts of reasoning models before discussing scaling approaches at both the training and inference levels.
Core Concepts
- A reasoning model doesn't equal human reasoning; it's a model that outputs intermediate reasoning traces and solves problems step-by-step.
- There are two main paths to improving reasoning: training scaling and inference-time scaling.
Training Level
- The article highlights RLVR (reinforcement learning with verifiable rewards): using outcome correctness as a reward for verifiable tasks.
- Verifiable domains are primarily math and coding, using tools like symbolic calculators, compilers, and unit tests to verify correctness.
- Training doesn't truly depend on the intermediate reasoning traces themselves; rewards are based mainly on the final answer and format.
- Driven solely by output rewards, models can learn to self-correct, backtrack, and revise—phenomena dubbed "Aha" moments.
Inference Level
- Extra compute can be applied during inference to improve answer quality, known as inference scaling.
- An example is self-consistency: sampling the same prompt multiple times and using majority voting to select the final answer.
- This method works for standard LLMs and can be layered on top of reasoning models.
Further Discussion
- The article notes that many modern LLMs have, in a sense, already become reasoning models.
- It introduces the concept of "different effort levels," allowing models to trade off speed, cost, and performance by controlling reasoning intensity.
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