When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning
This paper provides a theoretical analysis of in-context search in LLMs, modeling it as approximate inference over reasoning traces. It shows that when reflections reliably localize early mistakes, in-context search yields exponential improvements with only polynomial sequential attempts; otherwise, no asymptotic benefit over parallel sampling. Gains are robust, learnable, and connect to optimal policy in reinforcement learning.
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[Submitted on 7 Jul 2026]
Title:When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning
View a PDF of the paper titled When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning, by Yotam Wolf and 2 other authors
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Abstract:Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise solution attempts. We provide a theoretical analysis of in-context search by modeling it as approximate inference over reasoning traces, where the base model defines a prior and self-reflection provides feedback for posterior updates, and study the resulting inference-time sampling complexity - the number of sequential attempts needed to achieve high success probability. We show that when reflections reliably localize early mistakes, in-context search can yield exponential improvements over the base model, solving problems with exponentially small zero-shot pass rates using only a polynomial number of sequential attempts, whereas when this property fails, conditioning on past attempts offers no asymptotic benefit over parallel sampling. We further show that these gains are robust and learnable: approximate posterior updates suffice, and cross-entropy training on search rollouts recovers the required behavior with polynomial sample complexity. Finally, we show that under a stagewise abstraction of reinforcement learning with verifiable rewards, the optimal policy extension implements the same posterior reweighting rule. We validate key qualitative predictions of the theory on real large reasoning models.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2607.06720 [cs.AI]
(or arXiv:2607.06720v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.06720
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yotam Wolf [view email] [v1] Tue, 7 Jul 2026 18:36:04 UTC (746 KB)
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