arXiv:2602.02909v2 Announce Type: replace-cross Abstract: Inference-time scaling via chain-of-thought (CoT) reasoning is a major driver of state-of-the-art LLM performance, but it comes with substantial latency and compute costs. We address a fundamental theoretical question: how many reasoning tokens are required to solve a problem as input size grows? By extending the bounded attention prefix oracle (BAPO) model--an abstraction of LLMs that quantifies the information flow required to solve a task--we prove lower bounds on the CoT tokens required for three canonical BAPO-hard tasks: binary ma
The rapid advancement and widespread adoption of large language models are increasingly bottlenecked by inference costs, making efficiency a crucial research area.
Understanding the fundamental token complexity of chain-of-thought reasoning is vital for optimizing LLM performance and reducing operational expenses, impacting their practical deployment.
This research provides theoretical lower bounds on reasoning token requirements, offering a new framework for evaluating and designing more efficient AI systems.
- · AI researchers
- · Cloud providers
- · LLM developers
- · Inefficient LLM architectures
- · Users with high compute costs
More efficient LLMs become feasible, reducing the operational cost of AI applications.
Increased accessibility and deployment of AI agents due to lower inference expenses.
This could accelerate the development of more complex and autonomous AI systems, potentially impacting various industries faster than anticipated.
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Read at arXiv cs.LG