arXiv:2605.02427v3 Announce Type: replace Abstract: A recurring pattern in "reasoning without training" is that base LLMs already assign non-trivial probability mass to correct multi-step solutions; the bottleneck is locating these modes efficiently at inference time. Power sampling provides a principled way to bias decoding toward such modes by targeting p_theta(x)^alpha with alpha > 1, but practical approximations must account for future-dependent correction factors that determine which prefixes remain promising. We introduce Auxiliary Particle Power Sampling (APPS), a blockwise particle alg
The paper addresses a core bottleneck in current LLM development, namely the challenge of efficiently extracting correct multi-step solutions at inference time from models that already possess the underlying knowledge.
Improving LLM inference efficiency and accuracy without further training can significantly accelerate the deployment and capability of advanced AI models across various applications, making agentic systems more robust.
This research proposes a method to optimize LLM decoding, potentially leading to more reliable and powerful AI agents that can 'reason' more effectively in real-world scenarios.
- · AI developers
- · Companies deploying LLMs
- · AI Agents sector
- · Cloud compute providers
- · Inefficient LLM finetuning approaches
- · Competitors with less efficient inference methods
More sophisticated and reliable AI agents become feasible for complex tasks.
Reduced operational costs for AI applications due to more efficient inference, accelerating adoption.
Enhanced AI agent capabilities could lead to new forms of automation, impacting knowledge work and service industries.
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Read at arXiv cs.AI