Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
Today's AI systems have human-designed, fixed architectures and cannot
autonomously and continuously improve themselves. The advance of AI could
itself be automated. If done safely, that would accelerate AI development and
allow us to reap its benefits much sooner. Meta-learning can automate the
discovery of novel algorithms, but is limited by first-order improvements and
the human design of a suitable search space. The G\"odel machine proposed a
theoretical alternative: a self-improving AI that repeatedly modifies itself in
a provably beneficial manner. Unfortunately, proving that most changes are net
beneficial is impossible in practice. We introduce the Darwin G\"odel Machine
(DGM), a self-improving system that iteratively modifies its own code (thereby
also improving its ability to modify its own codebase) and empirically
validates each change using coding benchmarks. Inspired by Darwinian evolution
and open-endedness research, the DGM maintains an archive of generated coding
agents. It grows the archive by sampling an agent from it and using a
foundation model to create a new, interesting, version of the sampled agent.
This open-ended exploration forms a growing tree of diverse, high-quality
agents and allows the parallel exploration of many different paths through the
search space. Empirically, the DGM automatically improves its coding
capabilities (e.g., better code editing tools, long-context window management,
peer-review mechanisms), increasing performance on SWE-bench from 20.0% to
50.0%, and on Polyglot from 14.2% to 30.7%. Furthermore, the DGM significantly
outperforms baselines without self-improvement or open-ended exploration. All
experiments were done with safety precautions (e.g., sandboxing, human
oversight). The DGM is a significant step toward self-improving AI, capable of
gathering its own stepping stones along paths that unfold into endless
innovation.
6839e5a195a2f11f0c38a83a
Jenny Zhang
6839e5a195a2f11f0c38a83b
Shengran Hu
claimed_verified
2025-06-02T07:43:55.369Z
640530fd0ab5e22719fb199a
/avatars/6b023f496a2f8cb5f37ec617d84179b7.svg
Shengran HU
user
Shengran
6839e5a195a2f11f0c38a83c
Cong Lu
6839e5a195a2f11f0c38a83d
Robert Lange
6839e5a195a2f11f0c38a83e
Jeff Clune
Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
Today's AI systems have human-designed, fixed architectures and cannot
autonomously and continuously improve themselves. The advance of AI could
itself be automated. If done safely, that would accelerate AI development and
allow us to reap its benefits much sooner. Meta-learning can automate the
discovery of novel algorithms, but is limited by first-order improvements and
the human design of a suitable search space. The G\"odel machine proposed a
theoretical alternative: a self-improving AI that repeatedly modifies itself in
a provably beneficial manner. Unfortunately, proving that most changes are net
beneficial is impossible in practice. We introduce the Darwin G\"odel Machine
(DGM), a self-improving system that iteratively modifies its own code (thereby
also improving its ability to modify its own codebase) and empirically
validates each change using coding benchmarks. Inspired by Darwinian evolution
and open-endedness research, the DGM maintains an archive of generated coding
agents. It grows the archive by sampling an agent from it and using a
foundation model to create a new, interesting, version of the sampled agent.
This open-ended exploration forms a growing tree of diverse, high-quality
agents and allows the parallel exploration of many different paths through the
search space. Empirically, the DGM automatically improves its coding
capabilities (e.g., better code editing tools, long-context window management,
peer-review mechanisms), increasing performance on SWE-bench from 20.0% to
50.0%, and on Polyglot from 14.2% to 30.7%. Furthermore, the DGM significantly
outperforms baselines without self-improvement or open-ended exploration. All
experiments were done with safety precautions (e.g., sandboxing, human
oversight). The DGM is a significant step toward self-improving AI, capable of
gathering its own stepping stones along paths that unfold into endless
innovation.
The Darwin G\"odel Machine improves its coding capabilities through iterative self-modification and open-ended exploration, surpassing other approaches in benchmarks.
G\"odel machine self-improving AI coding benchmarks SWE-bench Polyglot sandboxing human oversight foundation model