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Reach or Solve? Attributing Agentic RL Gains with Checkpoint Handoffs
Reinforcement learning now trains language-model agents that act over dozens of steps in live environments. The gains are large, and they are read as better decision-making. An agent in a closed loop writes its own inputs. Each observation follows from its own earlier actions, so the states it meets
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