Recursive self-improvement (RSI) requires an agentic system to design, train, and evaluate its own successor. Data-driven post-training is a central part of that loop, yet existing evaluations measure
Recursive self-improvement (RSI) requires an agentic system to design, train, and evaluate its own successor. Data-driven post-training is a central part of that loop, yet existing evaluations measure
Details from Hugging Face ↗.
ParetoRSI-Gym: Can AI Agents Improve Language Models Across Multiple Domains? was first seen on Hugging Face · Daily papers on Oct 28, 2026. Since then it has appeared in 9 checks on 1 different day. Its best position was #16 of 30, reached on Oct 28, 03:28 UTC. It dropped off the list after Oct 28, 17:28 UTC.
This page has little history so far; it stays out of search results until the item has trended on two days or has more context.
Position on Hugging Face · Daily papers at every check (lower is better).
Best rank per day (UTC).
Platform metric as reported by Hugging Face.
Archived daily, weekly and monthly lists that include this entry.
Not at the moment. It was last on the list on Oct 28, 17:28 UTC, after 1 day of trending.
#16 on Hugging Face · Daily papers, on Oct 28, 03:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 28, 2026 and has been on the list on 1 day (9 checks) since.