How to Control Reasoning Effort in Large Language Models

Summary

The article explains how modern reasoning models are trained to spend different amounts of computation on a task and why higher effort can improve accuracy at rising cost. It defines a reasoning model as an LLM that produces an intermediate reasoning trace, while noting that <think> delimiters are formatting devices rather than the source of reasoning ability. DeepSeek-R1 showed that reinforcement learning with verifiable rewards (RLVR), using correctness checks for domains such as mathematics and code, can teach models to generate traces, backtrack, and self-correct; the trace itself was not used as the reward in that setup. The article distinguishes training scaling from inference scaling: training creates stronger models, while inference scaling spends more tokens or makes repeated attempts after training. Self-consistency is one example of the latter. Reasoning can also be enabled or disabled through chat templates, prompts, or prefilled empty reasoning blocks, as in Qwen3 and related systems. For models such as GPT-5.6, the author argues from public evidence that effort labels may be mapped to system instructions, with higher settings generally increasing token use and accuracy but eventually producing diminishing returns and higher API cost. Possible implementations include effort-conditioned RLVR with different token penalties, supervised fine-tuning on responses of different lengths, or a combination of both. The article compares disclosed recipes from DeepSeek V4, Nemotron 3 Ultra, Kimi K2.5 and K3, GLM-5, Qwen3, and Inkling. These methods include separate effort specialists, mixed thinking and non-thinking SFT data, hard or learned budgets, alternating constrained and unconstrained RL, multi-turn thinking controls, and continuous effort values. The models differ substantially, and public reports omit enough details to prevent a controlled comparison. The article concludes that model size and reasoning effort are separate cost-quality-latency controls, while automatic effort selection by a router or agent harness remains an unresolved goal.