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 d