Title: Ada-Search: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning
Primary area: foundation or frontier models, including LLMs
Keywords: Large language models, retrieval-augmented generation, search agent

Abstract: Equipping large language models (LLMs) with search engines via reinforcement learning (RL) has proven effective for building search agents.
However, overreliance on search introduces unnecessary cost and risks exposure to noisy or malicious content.
The central challenge is to develop agents that adaptively balance parametric knowledge with external search, invoking search only when necessary.
Prior work attempted to address overuse by tying rewards and evaluations to the number of search calls. However, shaping rewards based on search-call counts often involves complex reward engineering and provides ambiguous guidance:
agents may exploit it either by crafting stronger queries or by superficially reducing calls.
Naively employing search-call counts as an evaluation metric also conflates necessary and unnecessary calls, obscuring the measurement of true adaptive behavior.
In this work, we propose **AdaSearch**, a simple outcome-driven RL framework that disentangles and jointly optimizes two objectives:
(1) solving the task and
(2) deciding whether to invoke search.
Experiments with fine-grained metrics such as F1 show that **AdaSearch** improves boundary awareness of model knowledge, reducing unnecessary search calls while preserving task performance.

TL;DR: We propose a multi-task RL that bypass complex reward engineering for building better adaptive search agents.