Learning When to Sample: Confidence-Aware Selective Sampling for Efficient Chain-of-Thought Reasoning
arXiv:2603.08999v3 Announce Type: replace Abstract: Large language models (LLMs) can achieve strong reasoning performance through chain-of-thought (CoT) reasoning, yet they often generate unnecessarily long reasoning paths that incur high inference cost. Self-consistency-based approaches push accuracy higher still, but they require sampling and aggregating multiple reasoning trajectories, leading to substantial computational overhead. In this paper, we introduce a confidence-aware selective sampling framework that, at inference time, analyzes a single reasoning trajectory to adaptively determi
The increasing computational demands of advanced LLM reasoning, especially with self-consistency methods, necessitate more efficient inferencing techniques to make them practically viable.
This development addresses a critical bottleneck in deploying powerful LLM reasoning, potentially making complex AI agents more economic and scalable for broader applications.
The cost-efficiency and performance of sophisticated LLM reasoning tasks can now improve significantly, reducing computational overhead while maintaining or boosting accuracy.
- · AI developers
- · Cloud providers
- · Businesses adopting AI agents
- · Compute infrastructure providers
- · Inefficient LLM reasoning methods
Enhanced efficiency in LLM chain-of-thought reasoning reduces inference costs and speeds up deployment.
More sophisticated AI agents and applications become economically feasible, expanding the scope of AI automation.
The acceleration of AI agent development could lead to faster collapse of certain white-collar workflows, intensifying demand for robust, cost-effective AI systems.
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