Recursive self-improvement requires models not only to acquire useful training experience, but also to decide when their current experience has ceased to be useful. Existing methods often focus on wha
A-RSI improves recursive self-improvement by refreshing training tasks based on declining learning signals and using independently verifiable program-generated answers, raising benchmark performance without external labels.
Recursive self-improvement requires models not only to acquire useful training experience, but also to decide when their current experience has ceased to be useful. Existing methods often focus on what experience to generate next, while paying less attention to when fresh experience should be introduced. Many also construct training targets through self-consistency or majority voting, making supervision depend on the model's own uncertain predictions. We address these two questions separately: training dynamics determine when to acquire new experience, while the answers to new tasks remain independently checkable. A-RSI monitors reward variation and accuracy in training logs to refresh a…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
A-RSI: Adaptive Task Refresh for Zero-Data Multimodal Self-Improvement was first seen on Hugging Face · Daily papers on Oct 30, 2026. Since then it has appeared in 14 checks on 2 different days. Its best position was #2 of 30, reached on Oct 30, 15:28 UTC. It is #2 right now, holding steady at #2 over the last 6 checks.
Position on Hugging Face · Daily papers at every check (lower is better).
Best rank per day (UTC).
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Yes. As of Oct 31, 09:28 UTC it is #2, holding steady at #2 over the last 6 checks.
#2 on Hugging Face · Daily papers, on Oct 30, 15:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 30, 2026 and has been on the list on 2 days (14 checks) since.