Self-improving AI / Promptbreeder
Promptbreeder
“we present Promptbreeder, a general-purpose self-referential self-improvement mechanism that evolves and adapts prompts for a given domain.”
Promptbreeder is one of the projects on the self-improving AI map. Below is what it changes about itself, whether the change lasts, and whether it improves the improver, each with the line from its own source.
Where it sits
Columns: what it changes, from the surface to the deepest change. Rows: whether the change outlasts the run. A number is how many projects sit in that square. See it on the 3D map
- What it changes
- Prompts, memory, context“Driven by an LLM, Promptbreeder mutates a population of task-prompts, and subsequently evaluates them for fitness on a training set.”
- Does the change last
- Gone when the run ends“At this point the fittest individual from the whole of the evolutionary run was evaluated against the test set.”
- Does it improve the improver
- improves the task agent; no evidence the improver got better“the percentage of times that the different kinds of mutation operators resulted in an improvement when applied to a task-prompt in the population”
- Who sets the goals
- people set tasks and scoring“To determine the fitness of a task-prompt we evaluate its performance on a random batch of training data.”
- How it was tested
- tested on held-out tasks“the dataset was split into two equal training and test sets before the experiments were conducted”
Date is arXiv v1. PaLM 2-L evolves a population of task-prompts and, alongside them, the mutation-prompts used to rewrite them. Fitness is accuracy on training examples for a task the authors pick. The paper's ablation compares training fitness and finds that removing any self-referential operator is harmful under nearly all circumstances, with the greatest benefit from re-describing task-prompts at initialization; mutation-prompt mutation is one of the operators ablated. The first-order hyper-mutation prompt that rewrites mutation-prompts is a fixed instruction.
Does it count as RSI?
It depends on the definition. Under the four used on What counts as RSI, by what its own source says:
- Loose (Eliezer Yudkowsky, Intelligence Explosion Microeconomics (2013)): does not count
- Durable (Ren et al., self-improvement survey (2026)): does not count
- Strict (Roman Yampolskiy, From Seed AI to Technological Singularity (2015)): does not count
- Autonomous (Anthropic, When AI builds itself): does not count
How it got here
Earlier work in this catalog that Promptbreeder cites, oldest first. Under each: where that work sits on the map, then where Promptbreeder sits (changes in bold), and what Promptbreeder's own paper says about it. Its whole family on the citation map
Cites Schmidhuber, the Gödel machine 2003, STaR 2022, Large Language Models Can Self-Improve 2022, Self-Refine 2023, Voyager 2023
Cited by STOP 2023, ADAS 2024, SICA 2025, AlphaEvolve 2025, Darwin Gödel Machine 2025, Hyperagents 2026, Ren, self-improvement 2026, Weco AIDE² 2026, Self-HPO 2026, RSI-Opt 2026, Pu, recursive self-improvement 2026, Skill∞ 2026
In the same square
ADAS: Automated Design of Agentic Systems, AlphaEvolve, AREX-2, LM2LM, MLEvolve, ParetoRSI-Gym, PostTrainBench, Reflexion, Self-Refine, The AI Scientist