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Reach or Solve? Attributing Agentic RL Gains with Checkpoint Handoffs

This story is from 2026-09-18. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.19636v1 Announce Type: new Abstract: 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 fro…

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  1. 2026-09-18 04:00 · arXiv cs.AI
    Reach or Solve? Attributing Agentic RL Gains with Checkpoint Handoffs

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