DEVLOG

#1 - Why I Started SL-LLM-R

Project: SL-LLM-R

Introduction

I originally just wanted a project for the summer.

I had a few ideas, but most already existed or sounded like something I would abandon after a few days.

Then I started thinking about AI.

I use AI a lot for coding. It can understand large projects and solve difficult problems, but it can also repeat the same simple mistake several times. Even when it notices that something failed, it usually does not actually learn from it.

The context becomes longer, but the model itself stays almost the same.

Current models are trained on human-created data and updated through processes designed by humans. They can generate answers, but they normally cannot decide what to learn next, which decision caused a failure, or whether a change would damage another capability.

So I started thinking about a system that could learn from its own verified experiences.

That became SL-LLM-R.

The Idea

The core mechanism is called VGCP:

Verifier-Grounded Counterfactual Plasticity.

The simplified process is:

Save the state before a decision.
Let the model act.
Check the result with an external verifier.
Generate possible repairs.
Replay the same situation with and without each repair.
Compare the outcomes.
Keep only changes supported by evidence.

In simplified form:

Δ = E[Y(change) - Y(original)]

If the repaired version performs better across controlled tests, the change may be useful. If the evidence is weak, the system should not update.

The goal is controlled, local, reversible, and verifiable learning.

The PDF

I first planned to write a short document.

That did not go as planned.

Every answer created more questions: How do I save the exact state? How do I compare runs fairly? How do I prevent leakage, forgetting, broken rollbacks, or the system approving its own changes?

The document became the SL-LLM-R V1.2.2 specification.

It is now 135 pages long and contains the architecture, mathematics, safety rules, algorithms, experiments, and failure conditions.

The PDF does not prove that the system works. It defines what I need to build and what would count as failure.

Completed Changes

Defined the project idea.
Created the VGCP concept.
Wrote the V1.2.2 specification.
Started the initial implementation.

Next Steps

Continue the deterministic foundation.
Implement snapshots and rollback.
Build the proposal system.
Compare it against simple baselines.

Summary

I started with a few random project ideas, got annoyed by how intelligent and stupid AI can be at the same time, and ended up designing a system that should learn from verified mistakes.

It might become something important.

It might also be another stupid idea that takes over my entire summer holiday.

Right now, I do not know.

That is why I am building it.

Summary (AI)

SL-LLM-R is a pre-experimental project about controlled, verifier-grounded continual learning. No validated self-improvement result is currently claimed.

Note

I spent way too long writing this devlog and the PDF.

I also started coding parts of the project, and it was… different from what I expected.

More soon.