arXiv:2510.11170v2 Announce Type: replace Abstract: With the rise of reasoning language models and test-time scaling methods as a paradigm for improving model performance, substantial computation is often required to generate multiple candidate sequences from the same prompt. This enables exploration of different reasoning paths toward the correct solution, however, allocates the same compute budget for each prompt. Grounded on the assumption that different prompts carry different degrees of complexity, and thus different computation needs, we propose EAGer, a training-free generation method t
The proliferation of large language models and the increasing demand for efficient inference-time scaling methods drive the need for adaptive computation strategies.
This development could significantly reduce the computational burden and cost associated with advanced AI reasoning, making powerful models more accessible and sustainable.
AI models may no longer apply a uniform computational budget to all prompts, instead dynamically adjusting resources based on perceived complexity.
- · AI cloud providers
- · Companies deploying large language models
- · AI research and development
- · Inefficient inference solutions
- · Companies with high fixed compute costs
Adaptive inference methods become a standard component in commercial large language model deployments.
Reduced operational costs for AI services lead to broader adoption and new applications for complex reasoning models.
The definition of 'compute-efficient AI' shifts, rewarding models capable of dynamic resource allocation over brute-force scaling.
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Read at arXiv cs.LG