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arXiv · 2603.07887

Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference

Noah Golowich·Fan Chen·Dhruv Rohatgi·Raghav Singhal·Carles Domingo-Enrich·Dylan J. Foster·Akshay Krishnamurthy
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Abstract

Inference-time methods that aggregate and prune multiple samples have emerged as a powerful paradigm for steering large language models, yet we lack any principled understanding of their accuracy-cost tradeoffs. In this paper, we introduce a route to rigorously study such approaches using the lens of *particle filtering* algorithms such as Sequential Monte Carlo (SMC). Given a base language model and a *process reward model* estimating expected terminal rewards, we ask: *how accurately can we sample from a target distribution given some number of process reward evaluations?* Theoretically, we identify (1) simple criteria enabling non-asymptotic guarantees for SMC; (2) algorithmic improvements to SMC; and (3) a fundamental limit faced by all particle filtering methods. Empirically, we demonstrate that our theoretical criteria effectively govern the *sampling error* of SMC, though not necessarily its final *accuracy*, suggesting that theoretical perspectives beyond sampling may be necessary.

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Noah Golowich, Fan Chen, Dhruv Rohatgi, Raghav Singhal, Carles Domingo-Enrich, Dylan J. Foster, Akshay Krishnamurthy. 2026-03-09. Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference. https://arxiv.org/abs/2603.07887

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