ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large Language Models

TL;DR

ThinkDial is an open-source framework that enables controllable reasoning in large language models by switching between different operational modes, balancing computational effort and performance effectively.

Contribution

This paper introduces the first open-source end-to-end system implementing gpt-oss-style reasoning control with discrete modes using novel training paradigms.

Findings

Achieves 50-75% token reduction with minimal performance loss

Enables seamless switching between reasoning modes

Demonstrates strong generalization on out-of-distribution tasks

Abstract

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.