arXiv:2510.23486v2 Announce Type: replace Abstract: Large reasoning models (LRMs) often consume excessive tokens, inflating computational cost and latency. More broadly, in goal reaching sequential decision problems we often want to reach the goal quickly, and LRM reasoning can be viewed through this lens. We challenge the assumption that longer responses improve accuracy. By penalizing reasoning tokens using a discounted reinforcement learning setup (interpretable as a small token cost) and analyzing Blackwell optimality in restricted policy classes, we encourage concise yet accurate reasonin
The increasing scale and computational demands of large reasoning models necessitate new approaches to optimize efficiency, making this research timely as AI development matures.
This development offers a potential pathway to significantly reduce the operational costs and latency of AI models, making advanced reasoning more accessible and scalable across various applications.
The focus on 'concise yet accurate reasoning' shifts the AI optimization paradigm from sheer capability to efficiency, potentially leading to more resource-lean and faster AI systems.
- · AI developers
- · Cloud providers
- · Any industry using large AI models
- · AI-as-a-Service providers
- · Inefficient AI models
- · High-cost AI inference providers
Refined training methodologies will produce more efficient Large Reasoning Models (LRMs) that consume fewer tokens.
Reduced operational costs for AI will enable broader deployment of complex AI systems, potentially democratizing access to advanced AI capabilities.
The pursuit of efficiency could lead to a new generation of 'lean AI' that operates effectively on more constrained compute resources, impacting hardware design and embedded AI.
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Read at arXiv cs.LG