Thinking inside the brackets: Grammar-Constrained Decoding

LLM reasoning is a core driver of model performance, but it is costly. Practitioners typically use prompting and fine-tuning as axes of control to reduce the thinking-verbosity of reasoning models. We identify and exhibit grammar-constrained decoding (GCD) as a third axis of control. We structure reasoning traces by a small context-free grammar at decode-time (e.g. open a subgoal, derive, add a fact, check, close) which is compatible with free-text payloads. On GPQA-Diamond and AIME24, GCD on Qwen3.6-27B reduces median generated tokens by 98% and 94%, while holding or improving accuracy over chain of thought. While GCD reliably makes reasoning succinct, we further identify and characterise cases where controlling reasoning is detrimental, or ineffective.