ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified Beliefs
ANTE is an agentic Reinforcement Learning framework that trains coding agents to build internal world models. It formalizes knowledge as a belief state containing what has happened, what is true now, and what actions will achieve. This allows agents to predict outcomes before acting and learn effectively from execution feedback.