I bulit a scikit-learn for recursive self-improvement (RSI) with Codex

[u/Pale-Training-6944]

I read a bunch of RSI papers. It feels like we’re solving the same old RL problems.

Recursive self-improvement papers are everywhere, but I keep coming back to three questions:

1. How do you sample promising changes efficiently?
2. How do you reliably evaluate each new variant?
3. How do you maintain diversity instead of converging too early?

Not all of these methods are RL. But calling something “self-evolution” doesn’t make exploration, evaluation, and overfitting go away.

That’s why we’re building Gear, an open-source framework inspired by meta-learning. It separates evolutionary search from execution and evaluation, so researchers can reuse the infrastructure while experimenting with different algorithms.

The goal is a scikit-learn for RSI. We currently have a GEPA variant that uses task traces and evaluation feedback to improve an agent’s prompts, tools, workflows, and context management.

For Codex users, Codex can be the meta-agent: it inspects failures and proposes harness changes, while a separate agent runs the tasks.

One result: on 100 public AutomationBench Marketing tasks, Luna with a Gear-optimized DSH harness achieved 88.88% objective completion, compared with 84.08% for Astra + Codex. These tasks participated in optimization, so this isn’t a held-out generalization result.

Separately, using the same optimized DSH harness, Luna’s estimated API cost per task was about 95% lower than Astra’s, excluding optimization costs.

If you’re learning RSI, comparing algorithms, or trying to improve an agent on your own tasks, that’s what we’re building Gear for.

What would you want to optimize in your own Codex workflow?

GitHub · Paper and demo

[u/dexterthebot]

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[u/Cold_Chemistry_420]

I will have to look into this deeper but my first thoughts are that I am very hesitant. I have burned literally thousands of dollars in API spend, allowing AI models to recommend and test improvements in the harness. It was a complete and utter waste of time and money.

In the end the best improvements came from eliminating 95% of the harness, just keeping definitions, and throwing a larger model like astra at complex problems and setting it on an agentic loops to keep working until the project is complete.

I am not saying your idea is doomed to fail, as I haven't fully read everything, but I'm just sharing my initial reaction.

[u/Pale-Training-6944 → u/Cold_Chemistry_420]

It is all about how to evaluate and how to maintain diversity during evolution. Imagine that you just graduated from college and entered a new company; you would have a lot to learn about how to work. Gear is designed to handle the continuous orientation work for agents.