Reach or Solve? Deep Diving into Agentic RL Gains with Checkpoint Handoffs

arXiv:2609.19636v2 Announce Type: replace Abstract: 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 the ability to complete the task once there. Comparing agents only on the states each one reaches does not separate the two, since each agent is then scored on states selected by its own actions. To address this conflation, we introduce checkpoint h

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