Open-Ended Evolution of Self-Improving Agents
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This work presents the Darwin Gödel Machine (DGM), a system that advances the vision of self-improving AI by combining self-referential code modification with open-ended evolutionary search. Unlike the original Gödel machine, which requires provable benefits for code changes (a practically intractable constraint), the DGM adopts an empirical approach: it modifies its own codebase and evaluates improvements on coding benchmarks. Key contributions and findings:
Self-referential self-improvement loop: The DGM starts with a single coding agent that edits its own Python-based codebase to improve its ability to read, write, and execute code using frozen foundation models (FMs). Each modification is evaluated on benchmarks like SWE-bench and Polyglot, with only successful agents retained for further iterations.
Open-ended exploration via evolutionary archive: Inspired by Darwinian evolution, the system maintains an archive of all prior agents and samples parents based on performance and novelty. This enables exploration beyond local optima and supports continual innovation, including revisiting previously suboptimal variants that become valuable stepping stones later.
Empirical performance gains: Across 80 iterations, DGM boosts coding success on SWE-bench from 20.0% to 50.0% and on Polyglot from 14.2% to 30.7%, outperforming strong baselines that lack either self-improvement or open-endedness. Its best agents match or exceed leading human-designed, open-source coding agents.
Emergent tool and workflow improvements: Through self-improvement, DGM enhances its capabilities by evolving more granular editing tools, retry and evaluation mechanisms, history-aware patch generation, and code summarization for long contexts.
Generalization across models and tasks: Agents discovered by DGM generalize well when transferred across foundation models (e.g., Claude 3.5 to 3.7, o3-mini) and programming languages, demonstrating robust improvements not overfit to a particular setup.
Safety-conscious design: All experiments were sandboxed, monitored, and scoped to confined domains. The paper also discusses how future self-improvement systems could evolve safer, more interpretable behaviors if these traits are part of the evaluation criteria.
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