A Critical Look at Power Sampling when Test Time Sharpening LLMs
Reinforcement learning with verifiable rewards (RLVR) has substantially improved LLM reasoning, yet growing evidence suggests it mainly performs distribution sharpening: concentrating probability mass on reasoning paths the base model already produces. Recent work therefore seeks RLVR's gains without needing reward signals, by sampling from the power distribution $π_α\propto π_{\text{base}}^α$ via Markov chain Monte Carlo or sequential Monte Carlo. We show that these samplers fail to converge to $π_α$ under realistic compute budgets, and that their gains are largely recreated via simple Best-of-$N$ with appropriate grading . This suggests they succeed by selecting high-probability sequenc...
| Authors | Abiel Malkani Talwar, Christopher Nemeth, Henry Moss |
|---|---|
| Primary category | stat.ML |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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