Great paper if you are tracking progress in recursive self-improvement (RSI).

(bookmark it)

There is so much hype around RSI, so I think it's worth understanding why current models are not able to do this properly yet.

Issues range from "lack of creativity" of models to getting stuck in a local optimum.

This work tries to provide more insights into whether agents can really post-train other agents.

Here is the most interesting finding reported in the paper: "the agent’s training strategy is locked in at the very beginning, and the entire remaining budget is spent on local adjustments within the selected strategy."

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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