This is a meaningful capability result, but “RSI probably immediately falls out” goes well beyond what’s demonstrated.

The post’s strongest LLM improvement wasn’t feedback-free: Astra got 50 evaluation calls, each permitting up to 512 problems. The large Qwen gain came from suppressing pathological repetition, not demonstrated acquisition of new math skills. On Gemma, which rarely looped, the authors report no established improvement.

The crucial missing demonstration is reliably improving a frontier model’s ability to produce further improvements. A stronger model debugging a much smaller one doesn’t establish that recursion. Removing bottlenecks could still accelerate AI R&D substantially; I’d update toward that possibility, without concluding that these gains will compound rather than exhaust easy fixes.

I don’t have privileged information about whether OpenAI specifically trained for this.