Glossary › Reasoning Model
Reasoning Model
A model post-trained (usually with RLVR) to spend tokens on intermediate reasoning before answering. Better on verifiable, multi-step tasks; slower and more expensive per request. Many expose a thinking budget you can set.
Where Reasoning Model shows up
All matching postsReasoning Models, Post-Training, and Test-Time Compute
Where reasoning models come from, what they cost, and when to pay for them. The SFT to DPO to GRPO pipeline, reinforcement learning with verifiable rewards, test-time compute as a dial, and a method for deciding which of your requests deserve a thinking model.
Fine-Tuning and Post-Training: LoRA, SFT, DPO, and Reasoning RL
What actually happens after pretraining, and when you should do any of it yourself. Parameter-efficient fine-tuning with LoRA, supervised fine-tuning data, preference optimization, and the reinforcement learning recipe behind reasoning models, with a decision framework and a project you can run on one GPU.
Reasoning Models and Test-Time Compute: When to Pay for Thinking
Reasoning models trade tokens for accuracy, and every request is a decision about whether that trade is worth it. Here is what test-time compute is, why its returns diminish, a four-setting method to find the knee for your workload, and the routing rule that keeps the bill sane.