ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large
  Language Models
Authors: name: Qianyu He, name: Siyu Yuan, name: Xuefeng Li, name: Mingxuan Wang, name: Jiangjie Chen

Large language models (LLMs) with chain-of-thought reasoning have
demonstrated remarkable problem-solving capabilities, but controlling their
computational effort remains a significant challenge for practical deployment.
Recent proprietary systems like OpenAI's gpt-oss series have introduced
discrete operational modes for intuitive reasoning control, but the open-source
community has largely failed to achieve such capabilities. In this paper, we
introduce ThinkDial, the first open-recipe end-to-end framework that
successfully implements gpt-oss-style controllable reasoning through discrete
operational modes. Our system enables seamless switching between three distinct
reasoning regimes: High mode (full reasoning capability), Medium mode (50
percent token reduction with <10 percent performance degradation), and Low mode
(75 percent token reduction with <15 percent performance degradation). We
achieve this through an end-to-end training paradigm that integrates
budget-mode control throughout the entire pipeline: budget-mode supervised
fine-tuning that embeds controllable reasoning capabilities directly into the
learning process, and two-phase budget-aware reinforcement learning with
adaptive reward shaping. Extensive experiments demonstrate that ThinkDial
achieves target compression-performance trade-offs with clear response length
reductions while maintaining performance thresholds. The framework also
exhibits strong generalization capabilities on out-of-distribution tasks.