StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction

ArXi:2605.06642v1 Announce Type: new Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely purely reactive, which weakens both exploration and credit assignment over extended trajectories. In this work, we present Strategic Trajectory Abstraction (StraTA), a simple framework that