Sakana AI launches a recursive self-improvement lab in Tokyo to build autonomous AI systems that redesign themselves

Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀

https://sakana.ai/rsi-lab

Today, we are announcing the Sakana AI Recursive Self-Improvement (RSI) Lab: a dedicated research group in Tokyo tasked with redesigning the AI development process itself using AI.

While the industry increasingly speculates about the theoretical potential of self-improving AI, we’ve spent the last two years actively laying the foundations to make it a reality:

▪ LLM²: AI models automating research to invent better preference optimization algorithms. ▪ Darwin Gödel Machine: Agents autonomously rewriting their own codebase to double software-engineering performance. ▪ ShinkaEvolve: Hyper-sample-efficient program evolution that builds novel loss functions for MoE models. ▪ ALE-Agent: Reinforcement agents outperforming hundreds of human experts via self-learning. ▪ Digital Red Queen: Open-ended adversarial coevolution laying the groundwork for RSI in cybersecurity. ▪ The AI Scientist: Towards end-to-end automation of AI research, recently published in Nature.

Now, we are unifying these breakthroughs. The Sakana AI RSI Lab is officially tasked with building open-ended, adaptive architectures that collectively self-improve.

Human intelligence did not emerge from limitless resources; it was forged through the open-ended, compounding process of evolution operating under strict constraints. We are applying this exact principle to AI.

We believe recursive self-improvement is achievable on modest, sample-efficient compute. It shouldn’t be a winner-take-all asset locked inside hyperscale clusters, but a democratized public good.

We’re scaling our team to execute this mission. We are looking for frontier scientists and engineers who are entirely unsatisfied with the brute-force status quo. If you are ready to break away from standard benchmarking and build the self-improving future in Japan, come build with us.

Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀

https://sakana.ai/rsi-lab

Today, we are announcing the Sakana AI Recursive Self-Improvement (RSI) Lab: a dedicated research group in Tokyo tasked with redesigning the AI development process itself using AI.

While the industry increasingly speculates about the theoretical potential of self-improving AI, we’ve spent the last two years actively laying the foundations to make it a reality:

▪ LLM²: AI models automating research to invent better preference optimization algorithms. ▪ Darwin Gödel Machine: Agents autonomously rewriting their own codebase to double software-engineering performance. ▪ ShinkaEvolve: Hyper-sample-efficient program evolution that builds novel loss functions for MoE models. ▪ ALE-Agent: Reinforcement agents outperforming hundreds of human experts via self-learning. ▪ Digital Red Queen: Open-ended adversarial coevolution laying the groundwork for RSI in cybersecurity. ▪ The AI Scientist: Towards end-to-end automation of AI research, recently published in Nature.

Now, we are unifying these breakthroughs. The Sakana AI RSI Lab is officially tasked with building open-ended, adaptive architectures that collectively self-improve.

Human intelligence did not emerge from limitless resources; it was forged through the open-ended, compounding process of evolution operating under strict constraints. We are applying this exact principle to AI.

We believe recursive self-improvement is achievable on modest, sample-efficient compute. It shouldn’t be a winner-take-all asset locked inside hyperscale clusters, but a democratized public good.

We’re scaling our team to execute this mission. We are looking for frontier scientists and engineers who are entirely unsatisfied with the brute-force status quo. If you are ready to break away from standard benchmarking and build the self-improving future in Japan, come build with us.

Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀

https://sakana.ai/rsi-lab

Today, we are announcing the Sakana AI Recursive Self-Improvement (RSI) Lab: a dedicated research group in Tokyo tasked with redesigning the AI development process itself using AI.

While the industry increasingly speculates about the theoretical potential of self-improving AI, we’ve spent the last two years actively laying the foundations to make it a reality:

▪ LLM²: AI models automating research to invent better preference optimization algorithms. ▪ Darwin Gödel Machine: Agents autonomously rewriting their own codebase to double software-engineering performance. ▪ ShinkaEvolve: Hyper-sample-efficient program evolution that builds novel loss functions for MoE models. ▪ ALE-Agent: Reinforcement agents outperforming hundreds of human experts via self-learning. ▪ Digital Red Queen: Open-ended adversarial coevolution laying the groundwork for RSI in cybersecurity. ▪ The AI Scientist: Towards end-to-end automation of AI research, recently published in Nature.

Now, we are unifying these breakthroughs. The Sakana AI RSI Lab is officially tasked with building open-ended, adaptive architectures that collectively self-improve.

Human intelligence did not emerge from limitless resources; it was forged through the open-ended, compounding process of evolution operating under strict constraints. We are applying this exact principle to AI.

We believe recursive self-improvement is achievable on modest, sample-efficient compute. It shouldn’t be a winner-take-all asset locked inside hyperscale clusters, but a democratized public good.

We’re scaling our team to execute this mission. We are looking for frontier scientists and engineers who are entirely unsatisfied with the brute-force status quo. If you are ready to break away from standard benchmarking and build the self-improving future in Japan, come build with us.

Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀

https://sakana.ai/rsi-lab

Today, we are announcing the Sakana AI Recursive Self-Improvement (RSI) Lab: a dedicated research group in Tokyo tasked with redesigning the AI development process itself using AI.

While the industry increasingly speculates about the theoretical potential of self-improving AI, we’ve spent the last two years actively laying the foundations to make it a reality:

▪ LLM²: AI models automating research to invent better preference optimization algorithms. ▪ Darwin Gödel Machine: Agents autonomously rewriting their own codebase to double software-engineering performance. ▪ ShinkaEvolve: Hyper-sample-efficient program evolution that builds novel loss functions for MoE models. ▪ ALE-Agent: Reinforcement agents outperforming hundreds of human experts via self-learning. ▪ Digital Red Queen: Open-ended adversarial coevolution laying the groundwork for RSI in cybersecurity. ▪ The AI Scientist: Towards end-to-end automation of AI research, recently published in Nature.

Now, we are unifying these breakthroughs. The Sakana AI RSI Lab is officially tasked with building open-ended, adaptive architectures that collectively self-improve.

Human intelligence did not emerge from limitless resources; it was forged through the open-ended, compounding process of evolution operating under strict constraints. We are applying this exact principle to AI.

We believe recursive self-improvement is achievable on modest, sample-efficient compute. It shouldn’t be a winner-take-all asset locked inside hyperscale clusters, but a democratized public good.

We’re scaling our team to execute this mission. We are looking for frontier scientists and engineers who are entirely unsatisfied with the brute-force status quo. If you are ready to break away from standard benchmarking and build the self-improving future in Japan, come build with us.

Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀

https://sakana.ai/rsi-lab

Today, we are announcing the Sakana AI Recursive Self-Improvement (RSI) Lab: a dedicated research group in Tokyo tasked with redesigning the AI development process itself using AI.

While the industry increasingly speculates about the theoretical potential of self-improving AI, we’ve spent the last two years actively laying the foundations to make it a reality:

▪ LLM²: AI models automating research to invent better preference optimization algorithms. ▪ Darwin Gödel Machine: Agents autonomously rewriting their own codebase to double software-engineering performance. ▪ ShinkaEvolve: Hyper-sample-efficient program evolution that builds novel loss functions for MoE models. ▪ ALE-Agent: Reinforcement agents outperforming hundreds of human experts via self-learning. ▪ Digital Red Queen: Open-ended adversarial coevolution laying the groundwork for RSI in cybersecurity. ▪ The AI Scientist: Towards end-to-end automation of AI research, recently published in Nature.

Now, we are unifying these breakthroughs. The Sakana AI RSI Lab is officially tasked with building open-ended, adaptive architectures that collectively self-improve.

Human intelligence did not emerge from limitless resources; it was forged through the open-ended, compounding process of evolution operating under strict constraints. We are applying this exact principle to AI.

We believe recursive self-improvement is achievable on modest, sample-efficient compute. It shouldn’t be a winner-take-all asset locked inside hyperscale clusters, but a democratized public good.

We’re scaling our team to execute this mission. We are looking for frontier scientists and engineers who are entirely unsatisfied with the brute-force status quo. If you are ready to break away from standard benchmarking and build the self-improving future in Japan, come build with us.

Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀

https://sakana.ai/rsi-lab

Today, we are announcing the Sakana AI Recursive Self-Improvement (RSI) Lab: a dedicated research group in Tokyo tasked with redesigning the AI development process itself using AI.

While the industry increasingly speculates about the theoretical potential of self-improving AI, we’ve spent the last two years actively laying the foundations to make it a reality:

▪ LLM²: AI models automating research to invent better preference optimization algorithms. ▪ Darwin Gödel Machine: Agents autonomously rewriting their own codebase to double software-engineering performance. ▪ ShinkaEvolve: Hyper-sample-efficient program evolution that builds novel loss functions for MoE models. ▪ ALE-Agent: Reinforcement agents outperforming hundreds of human experts via self-learning. ▪ Digital Red Queen: Open-ended adversarial coevolution laying the groundwork for RSI in cybersecurity. ▪ The AI Scientist: Towards end-to-end automation of AI research, recently published in Nature.

Now, we are unifying these breakthroughs. The Sakana AI RSI Lab is officially tasked with building open-ended, adaptive architectures that collectively self-improve.

Human intelligence did not emerge from limitless resources; it was forged through the open-ended, compounding process of evolution operating under strict constraints. We are applying this exact principle to AI.

We believe recursive self-improvement is achievable on modest, sample-efficient compute. It shouldn’t be a winner-take-all asset locked inside hyperscale clusters, but a democratized public good.

We’re scaling our team to execute this mission. We are looking for frontier scientists and engineers who are entirely unsatisfied with the brute-force status quo. If you are ready to break away from standard benchmarking and build the self-improving future in Japan, come build with us.