AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning
AdaSearch is proposed, a simple two-stage, outcome-driven RL framework that disentangles problem-solving from the decision to search, making the decision process explicit and interpretable and significantly improves search-decision quality and reduces unnecessary search calls.