title: Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
summary: 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.
paper_authors._id: 6839e5a195a2f11f0c38a83a
paper_authors.name: Jenny Zhang
paper_authors._id: 6839e5a195a2f11f0c38a83b
paper_authors.name: Shengran Hu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:43:55.369Z
paper_authors._id: 640530fd0ab5e22719fb199a
paper_authors.avatarUrl: /avatars/6b023f496a2f8cb5f37ec617d84179b7.svg
paper_authors.fullname: Shengran HU
paper_authors.type: user
paper_authors.user: Shengran
paper_authors._id: 6839e5a195a2f11f0c38a83c
paper_authors.name: Cong Lu
paper_authors._id: 6839e5a195a2f11f0c38a83d
paper_authors.name: Robert Lange
paper_authors._id: 6839e5a195a2f11f0c38a83e
paper_authors.name: Jeff Clune
paper_title: Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
paper_summary: 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.
paper_ai_summary: The Darwin G\"odel Machine improves its coding capabilities through iterative self-modification and open-ended exploration, surpassing other approaches in benchmarks.
paper_ai_keywords: G\"odel machine self-improving AI coding benchmarks SWE-bench Polyglot sandboxing human oversight foundation model