Can AI systems improve themselves through trial and error?

The original Gödel Machine proposed self-improving AI via provably beneficial self-modifications. In practice, formally proving the impact of most self-modifications is impossible. The Darwin Gödel Machine (DGM) replaces formal proofs with empirical validation: try modifications, test them on benchmarks, keep what works. This mirrors biological evolution — mutations are not verified in advance but produced, trialed, and selected.

DGM alternates between self-modification and evaluation phases. During self-modification, agents from the archive generate modified versions of themselves — rewriting their own code. During evaluation, each modified agent is tested on coding benchmarks. The key assumption: improvement on coding benchmarks indicates better coding capabilities, which in turn indicates better ability to self-modify. This creates a meta-competence loop: better coding → better self-modification → better coding.

Results: SWE-bench from 20.0% to 50.0%, Polyglot from 14.2% to 30.7%.

The evolutionary archive is critical. Inspired by open-endedness research, DGM maintains a growing library of all generated agent variants — including suboptimal but interesting ones. These serve as stepping stones for future generations, enabling diverse exploration paths. The system doesn't just optimize for immediate performance; it accumulates diverse capabilities that may enable future breakthroughs. This is fundamentally different from single-trajectory self-improvement.

Concrete improvements discovered include better code editing tools, long-context window management, and peer-review mechanisms — capabilities the original agent lacked that emerged through the self-improvement process.

The Python-based implementation makes the self-modification space Turing-complete in principle. The current version modifies agent design (tools, workflows) with frozen foundation models. Full self-improvement — rewriting training scripts, training new foundation models — is left as future work.

This directly addresses What limits how much models can improve themselves?: DGM circumvents the formal proof requirement by using empirical validation, but inherits a different limitation — improvement is bounded by what the benchmark can measure. The archive approach partially addresses How quickly do errors compound during model self-training? by maintaining diverse populations rather than following single improvement trajectories.