AdapThink: Adaptive Thinking Preferences for Reasoning Language Model

TL;DR

AdapThink introduces an adaptive post-training framework for reasoning language models that dynamically balances reasoning depth and efficiency, improving performance on mathematical reasoning tasks.

Contribution

It proposes a novel adaptive mechanism combining confidence-based rewards and diversity-aware sampling to enhance reasoning efficiency and effectiveness.

Findings

Improves reasoning efficiency by reducing unnecessary computation.

Maintains or enhances accuracy on mathematical reasoning datasets.

Enables dynamic adjustment of reasoning depth based on question complexity.

Abstract

Reinforcement Learning (RL)-based post-training has significantly advanced the complex reasoning capabilities of language models, fostering sophisticated self-reflection processes. However, this ``slow thinking'' paradigm presents a critical challenge to reasoning efficiency: models may expend excessive computation on simple questions and shift reasoning prematurely for complex ones. Previous mechanisms typically rely on static length budgets or predefined rules, lacking the adaptability for varying question complexities and models' evolving capabilities. To this end, we propose AdapThink, an adaptive post-training framework designed to induce more efficient thinking while maintaining the performance of reasoning language models. Specifically, AdapThink incorporates two key mechanisms: 1) A group-relative reward function that leverages model confidence and response's characteristic to dynamically adjust the preference of reflection-related transition words without resorting to a fixed length preference. 2) A diversity-aware sampling mechanism that balances the training group's solution accuracy with reasoning diversity via an entropy-guided score. Experiments on several mathematical reasoning datasets with DeepSeek-distilled models demonstrate AdapThink's advantages in enabling adaptive reasoning patterns and mitigating the inefficiencies.