Sakana AI Launches RSI Lab: Recursive Self-Improvement with Foundation Models

Sakana AI has formally established its Recursive Self-Improvement (RSI) Lab, a dedicated research group tasked with redesigning the AI development process itself using AI. Rather than brute-forcing monolithic models, the lab builds open-ended, adaptive architectures that collectively self-improve — drawing on a lineage of published milestones.

Key Research Milestones Backing RSI

• LLM-Squared (2024): Developed with Oxford and Cambridge, this framework lets LLMs invent better ways to train LLMs (LLM²). It produced DiscoPOP, a preference optimization algorithm discovered and written entirely by an LLM through a generational evolutionary loop.
• Darwin Gödel Machine (2025): In collaboration with UBC, DGM maintains an evolving lineage of agent variants that autonomously rewrite their own codebase. On SWE-bench, it more than doubled baseline performance — a 30 percentage point absolute improvement.
• ShinkaEvolve (2025): Open-source framework demonstrating sample-efficient program evolution. Solved complex optimization problems using only 150 samples and generated a novel load-balancing loss function improving Mixture-of-Experts (MoE) models.
• ALE-Agent (2025): Optimization agent that secured 1st place out of 804 human participants in AtCoder Heuristic Contest 058. It leverages massive inference-time scaling and self-learning from trial-and-error failures to autonomously derive novel algorithms.
• Digital Red Queen (2026): Collaboration with MIT establishing open-ended adversarial coevolution in Core War. LLMs author competing code, driving emergent complex software strategies and convergent evolution — foundational for cybersecurity RSI.
• The AI Scientist (2024–2026): Fully automated open-ended scientific discovery, from idea generation, experiment execution, full paper writing, to peer review.

Why This Matters for Developers

RSI represents a shift from static, human-led R&D to autonomous self-improving intelligence engines. The lab's approach — evolutionary optimization loops, self-rewriting agents, and automated science — directly impacts how AI coding agents are built and improved. Rather than waiting for manual tuning, these systems continuously refine their own architectures.