ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large
  Language Models
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.
68ae774a364411bea07df76a
Qianyu He
68ae774a364411bea07df76b
Siyu Yuan
68ae774a364411bea07df76c
Xuefeng Li
68ae774a364411bea07df76d
Mingxuan Wang
68ae774a364411bea07df76e
Jiangjie Chen
ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large
  Language Models
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.
ThinkDial is an open-source framework that implements controllable reasoning in large language models through discrete operational modes, achieving performance while reducing computational effort.
chain-of-thought reasoning large language models discrete operational modes ThinkDial budget-mode supervised fine-tuning two-phase budget-aware reinforcement learning adaptive reward shaping