arXiv:2507.14958v5 Announce Type: replace Abstract: Large Language Models have achieved impressive performance on reasoning-intensive tasks, yet optimizing their reasoning efficiency remains an open challenge. While Test-Time Scaling (TTS) improves reasoning quality, it often leads to overthinking, wasting tokens on redundant computations. This work investigates how to efficiently and adaptively guide current model' test-time scaling without additional training. Inspired by the concept of momentum in physics, we propose Momentum Uncertainty-guided Reasoning (MUR), which dynamically allocates t
The accelerating performance of Large Language Models (LLMs) is pushing the demand for more efficient reasoning methods to reduce computational overhead without sacrificing quality.
Improving the efficiency of LLM reasoning directly impacts the cost and scalability of AI applications, making advanced AI more accessible and sustainable.
This research introduces a novel, adaptive method to optimize LLM reasoning, potentially leading to more cost-effective and faster deployment of sophisticated AI systems.
- · AI developers
- · Cloud providers
- · Users of LLM-powered applications
- · AI models with high token waste
- · Companies relying on inefficient LLM scaling
More efficient LLMs will reduce operational costs for AI companies and increase the viability of complex AI applications.
Reduced computational demands could mitigate some of the energy consumption concerns associated with large-scale AI deployment.
Widely adopted efficient reasoning techniques might accelerate the development and deployment of more capable AI agents across various industries.
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