arXiv:2605.26849v1 Announce Type: new Abstract: Sampling multiple responses improves language model reasoning, but uniform compute allocation is inefficient: easy questions are over-sampled while hard questions remain under-explored. We propose Uncertainty-Aware Budget Allocation (UAB), a concave integer optimization framework that reallocates a fixed sampling budget based on per-question uncertainty estimated at no additional inference cost. In Phase 1, every question receives one generation; its average negative log-likelihood (ANLL), extracted directly from output log-probabilities, serves
The rapid development and deployment of large language models are creating an urgent need for more efficient resource allocation to optimize their performance and reduce operational costs, especially as models become larger and more complex.
This development allows for more intelligent and dynamic compute allocation in AI systems, directly improving the efficiency and effectiveness of language model reasoning, which is critical for scaling AI applications.
Instead of uniform compute allocation, language models can now dynamically adjust their sampling budget based on the difficulty of the task, leading to more efficient resource use and potentially faster, more accurate results.
- · AI developers
- · Cloud computing providers
- · Organizations deploying large language models
- · General AI research
- · Inefficient AI systems
- · Wasteful compute practices
Reduced operational costs and improved performance for large language models will become more accessible.
This efficiency gain could accelerate the development and widespread adoption of more complex and autonomous AI agents.
More efficient AI reasoning could lead to breakthroughs in areas requiring extensive computational search, potentially impacting scientific discovery and complex problem-solving domains.
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