Scaling test-time computation improves the reasoning ability of large language models (LLMs), but the resulting latency and cost limit its use in latency- and cost-sensitive scenarios. We present BudgetThinker, a framework for reasoning-budget adherence that provides online control of chain-of-thought (CoT) length during decoding. Rather than relying on a one-shot prompt instruction, BudgetThinker periodically inserts specialized tokens that communicate the remaining reasoning budget. A two-stage training pipeline, consisting of supervised fine-tuning and curriculum-based reinforcement learning, teaches the model to interpret these signals and adapt its reasoning pace. Across the evaluated 1.5B and 7B text backbones, a 7B multimodal backbone, and a 1.5B code-reasoning model evaluated on HumanEval, BudgetThinker improves budget adherence and accuracy–efficiency trade-offs over competitive baselines. The controlled comparisons isolate the contribution of online ratio signaling from the shared training pipeline. Code is available at https://github.com/MobileLLM/BudgetThinker.