If the weights never change, is it really recursive self-improvement?
▲ 54 r/reinforcementlearning+1 crossposts

If the weights never change, is it really recursive self-improvement?

https://preview.redd.it/e0ydm43a55kh1.png?width=2902&format=png&auto=webp&s=8c9b88ff5e4157811e8996ba5a1e96cc55c8ae6a

This paper is using a much narrower definition of recursive self-improvement than the phrase usually suggests.

AQuA stores validated evidence in a persistent research state that shapes later hypotheses. The underlying language model and evaluator remain fixed. I still find the narrower claim interesting, even if it sits closer to memory-augmented research automation than to a model rewriting itself.

The paper does not establish any weight-level capability gain. Is persistent memory that improves later research decisions enough to call a system RSI, or should the term require changes to the system’s underlying capabilities?

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u/derspenti — 2 days ago

Agent memory layers don't need an LLM deciding what to remember

Most agent memory setups run a model call on the way in. Something reads the turn, decides whether it's worth keeping, rewrites it into a "memory", tags it with a type and an importance score. That's a second inference pass on every interaction and I think it's the wrong place to spend one.

Cost isn't the issue. The judgment being unauditable is. When the agent fails to recall something you can't tell whether retrieval missed it or the extractor decided six days ago it wasn't worth keeping. Two different bugs, same symptom, and separating them means reading logs that mostly don't exist.

Take that stage out and you have storage, an embedding, retrieval. Which is what a memory layer is. I run memU partly for this reason. Core is around 500 lines doing those three things, exposed as commit, list, retrieve. What it keeps is readable Markdown, embedded and indexed into a local sqlite db. Apache-2.0.

You lose the distillation, obviously. Raw turns are noisier than summarised ones so retrieval has to be better to compensate. I'll take that trade because a noisy store you can read beats a tidy one you can't inspect, but I'd take the other side seriously from anyone who has actually measured it.

Being straight about the cost since someone will find it anyway: self-hosting does not get you off the hook for an embedding provider, you still need a key for that. It's single machine too, syncing across boxes is their hosted mode. And recall quality is entirely downstream of whichever embedder you point it at. With a small one the recalls go vague on anything abstract. Facts and procedures come back fine. A preference I mentioned once in passing, much less reliably.

Anyone still running the extraction step in their harness and finding it earns its keep? Would like to know what it's buying that retrieval isn't.

u/derspenti — 15 days ago

Everyone posts day-one impressions. What's still in your stack a month later?

Day one threads are the least useful thing we produce here and we produce a lot of them. Model drops, forty people run their favourite prompt, half say it's the best thing ever and half say benchmaxxed, and none of that survives contact with two weeks of real work.

So: what did you install in the last month or two that's still in the rotation, and what quietly got uninstalled?

I'll go first. Still here: Qwen3.6 27B for anything that has to actually know something. Ling-3.0-flash sitting in the executor slot of my agent setup, which surprised me because I only put it there expecting to watch it fail and it hasn't yet, and officially confirmed open source soon (now is free on open router). Gone: two things I was very excited about on day one, which I'm not naming because I don't want that argument in this thread.

What I'd like to hear is the boring version. Not "X is amazing", but "X is still doing Y for me on Z and I've stopped thinking about it". A model you've stopped thinking about is the highest praise available.

Also interested in the reverse. Stuff that got worse for you over time, or that you kept using out of inertia and then finally dropped. That never shows up in the day one threads either

u/derspenti — 22 days ago