Finally a good paper testing whether agents can really post-train other agents.

(bookmark it)

They analyzed a large corpus of publicly released post-training trajectories. Across tasks, the agent locks in its training strategy at the very first step and spends the entire remaining budget on local adjustments inside it.

They then tried three escalating fixes. An experience-driven scaffold lifted execution broadly, worth 12.6 points on GSM8K and 40.8 on HumanEval, and the strategy stayed frozen.

Human guidance redirected the opening choice, and the agent slid back into local loops once training began. Extra inference compute paid off on easy tasks and did almost nothing on the hardest one.

What agents lack here is a way to reconsider strategy while execution is still running.

Paper: https://arxiv.org/abs/2608.19072

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