Controlling Reasoning Effort in LLMs

The article surveys techniques for adjusting how much reasoning a model performs, building on OpenAI's o1 and DeepSeek-R1. It explains the reinforcement learning with verifiable rewards (RLVR) approach used to train such reasoning models. Sebastian Raschka also highlights open questions in balancing reasoning depth and cost.