AReaL-RSI: Adaptive Scaling and Coordination of Recursive Self-Improvement

Scaling recursive self-improvement requires additional computation to produce useful research progress in reaching persistent objectives. Intuitively, more re- searcher agents can explore more hypotheses, but early evaluation scores might misrepresent their promise, shared findings could lose their applicability condi- tions, and useful procedures could remain confined to completed investigations. In this paper, we present AReaL-RSI, a framework for organizing a persistent research team of agents without updating backbone LLM weights. We formu- late the scaling problem in terms of evaluated outcomes and whole-system re- source expenditure. Concretely, a leader agent selects task-appropriate research orientations and guides ongoing investigations; conditional memory sharing dis- tinguishes unresolved problems from supported findings and enables recipient adaptation. During recursive evolution, after each campaign, evidence-based re- view revises researcher orientation, knowledge, and skills for later campaigns. Experiments compare AReaL-RSI with Codex-Goal on downstream harness optimization, GPU kernel optimization, and AI research tasks using two back- bones. Under the reported resource allocations, AReaL-RSI improves Terminal Bench performance by 2.2 percentage points for each backbone and increases mean KernelBench speedup by 32.2–36.8%; further scalability comparisons fa- vor the design of AReaL-RSI over native scaling: combining experience sharing with specialized researcher roles yields 1.9–27.5% lower kernel latency than in- dependent exploration and 1.3–33.9% lower latency than sharing alone across all seven comparable task–backbone pairs.