AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning

Abstract:Equipping large language models (LLMs) with search engines via reinforcement learning (RL) promises effective search agents. However, adaptively balancing internal parametric knowledge with external search remains a challenge, as overreliance on search introduces unnecessary cost and risks exposure to noisy or malicious content, while relying solely on parametric knowledge risks hallucination. Prior efforts mitigate search overuse through tool-call reward shaping, which requires heavy reward engineering and conflates necessary and unnecessary search. To address these limitations, we revisit the evaluation of search agents through an F1-based decision metric, revealing that prior methods often overlook readily available parametric knowledge. Motivated by this, we propose AdaSearch, a simple two-stage, outcome-driven RL framework that disentangles problem-solving from the decision to search, making the decision process explicit and interpretable. Extensive experiments demonstrate that AdaSearch significantly improves search-decision quality and reduces unnecessary search calls, with only a small trade-off in QA accuracy relative to always-search.

Accepted to EMNLP 2026. Code and artifacts will be uploaded to this https URL

Computation and Language (cs.CL)

arXiv:2512.16883 [cs.CL]

(or arXiv:2512.16883v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2512.16883 Focus to learn more arXiv-issued DOI via DataCite

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