self-improvement

Google Cloud AI Research and several universities introduced RRSI, a method that stops self-optimizing agent harnesses from memorizing their test tasks. According to the paper, it gains up to 4.7 points on five unseen benchmarks and uses about 30% fewer runtime tokens than the unregularized version, with the underlying model frozen.

Researchers at Meta, University of British Columbia, and partner institutions have developed hyperagents—AI systems that optimize both their task performance and the mechanisms controlling their self-improvement. Unlike previous self-improvement approaches locked to coding tasks, DGM-Hyperagents (DGM-H) demonstrate significant gains across four domains and can transfer improvement strategies to entirely new tasks.