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Reach or Solve? Deep Diving into Agentic RL Gains with Checkpoint Handoffs
Reinforcement learning (RL) is widely used to improve language-model agents, and its gains are usually measured by final task success. However, an agent's earlier actions shape the states in which its later decisions are made, so final task success conflates the ability to reach useful states with t
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