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StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction
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 pres
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