The Model Knows, the Decoder Finds: Future Value Guided Particle Power Sampling
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 algorithm for approximating the sequence-level power target with a bounded population of partial solutions. APPS propagates hypotheses in parallel using...
| Authors | Tu Nguyen, Matthieu Zimmer, Rasul Tutunov et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 May 2026 |
| Last updated at source | 13 June 2026 |
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