arXiv:2606.19919v1 Announce Type: new Abstract: Large reasoning models rely on long chain-of-thought to achieve strong performance, but applying such reasoning uniformly incurs high computational cost. Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability. We identify the root cause as sequence-level coupling between efficiency incentives and correctness optimization, which implicitly penalizes long but correct reasoning trajectories. To address this issue, we propose Adaptive Dual-Process Thinking (ADaPT), a token-level dua
The increasing scale and computational cost of large reasoning models necessitate new efficiency paradigms to sustain progress and broader application.
Efficient large reasoning models can make advanced AI capabilities more accessible and deployable, reducing the financial and computational barriers to their use and development.
The proposed ADaPT method fundamentally alters how computational resources are allocated within large reasoning models by decoupling efficiency from correctness at a token level.
- · AI developers
- · Cloud providers
- · Companies using AI for complex tasks
Reduced operational costs and increased speed for AI-powered reasoning tasks.
Democratization of advanced AI capabilities, leading to more widespread adoption across industries.
Accelerated innovation in AI agent design and deployment due to more efficient inference at scale.
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