Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling
Power-sharpened sampling is an inference-time alternative to reinforcement-learning (RL) post-training for enhancing reasoning in large language models (LLMs). High-probability sequences are amplified under the base model without parameter updates or external rewards, avoiding the costly optimization and jagged general...
Panagiotis Theodoropoulos, Nan Jiang, Xin-Tong Duan et al.
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