2026
Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs
ICLR 2026poster
While search-augmented large language models (LLMs) exhibit impressive capabilities, their reliability in complex multi-hop reasoning remains limited. This limitation arises from three fundamental challenges: decomposition errors, where tasks are incorrectly broken down; retrieval missing, where key…