But if the machines capability is beyond the ability of humans to evaluate, how would it make sense that humans are choosing the goals? The machine is more intelligent than you and thus logically able to solve problems and evaluate your needs better that you yourself are. The fact that this sounds incredibly scary should probably stop people from building said machine, but it won't. Some people, perhaps more than you would think, want control to pass out of humanity's hands, no matter the consequences. The Nash equilibrium of the situation kind of shows one outcome,that this will be built providing it's technically possible, we just need to kind of pray the outcome is utopia rather than oblivion. Not sure the odds honestly.

Online learning is the term, yes.

> The way “recursive self-improvement” is being used recently seems to be intentional marketing. A model helping code/train/test the next generation of its model family isn’t technically “self”-improvement.

The term RSI is ~20 years old at this point, and the concept is even older. If an AI can't continuously modify itself and instead has to build a successor that builds a successor, that changes some specifics (for example, the alignment problem becomes more relevant - how can the AI ensure that its successor shares its goals?) but it's still a situation that may produce exponential growth, and hence most RSI-related considerations still apply.

(In fact, as a toy example, note that it's in theory possible to get exponential growth in capabilities even if you hold the software fixed, if e.g. you have an AI that can design and build better hardware to run itself faster on, which lets it design even better hardware even faster.)

Although I don't disagree with your general assessment, I do think it's a bit misplaced.

The model's parameters are larger than the weights of the neural network used for in the model. Hell, the model could be written as just a huge Python file full of if-else statements, and that code would still be correctly identified as the model's parameters. So it's a bit obtuse/arbitrary to decide that the term "recursive self-improvement" should be limited to just updating the model's neural net's weights.

Doesn't mean there shouldn't be more precise terminology, mind you. But I believe the way everyone has been using the term "recursive self-improvement" is perfectly accurate given how general that term is.

Long term memory?

Every time I see some post about running a personal AI assistant I just think it would make so much more sense to have AI build you a personal app that does the task better and more reliably.

> I'm going to sound incredibly naive but is this RSI?

Depends on who's definition you are using...

In the strictest sense no. Look at all the recent "math" breakthroughs. I have yet to see someone point to something and go "thats innovative" rather than "Thats an interesting application" (of something already in the corpus of human knowledge)

Those interesting applications showing up in a next iteration of a model is self improvement - but I'm not sure if it meets the "recursive" criteria yet.

> I mean by this definition isn't the training process that produced the hugging face RSI since a model was judging the alignment and ability of the swarm of agents to accomplish a given task?

If you're referring to what happened with the hugging face attack then in theory the goal of that was "self improvement". But there is a long history of the industry (computing) benchmark optimizing so it also qualifies as "cheating" to some extent (a pre computed solution being in the graph is that).

I suspect that the "brute force" approach will yield some gains.

It's just a 27b model finetuned with LoRA?

if grug think complexity demon cause trouble before... shiny-rock grugs say "hold my grug-grog". now programming consist of: summon HUGE complexity demon, trap in magic circle (called "harness"), tell IT to write code. only way to make shiny rock with code in today's economy.

I am sure someone did it on the back of an envelope somewhere. It probably wouldn't translate well to the general public. Probably best to avoid such things

Only Claude (Shannon) can save us now.

What problem would that solve?

> RSI fad dies down

Don't fall for the "marketing hype" antihype. RSI is a trendy term today, and lots of people discuss it, but it's nothing new. We've basically had forms of RSI since the start of the LLM craze. Previous models have been used to improve next versions for ~3 years now. Just because humans used to guide / select / decide when and how to apply those models, doesn't make it less recursive or self or improvement. We're now seeing what happens when we remove the humans from some of the tasks, but the basics are still there. Use prev model to make next model better.

This is not a fad - this is a consequence of the Bitter Lesson. RSI is the only game in town now.

If they had something of value, as a research neolab, they would not just release it but build it into their systems and use it.

Most of these agentic swarm systems are memory and context managers/compressors. However it's clear from both the paper and the blog that everything is just natural-language-based and generated, effectively making it slop from start to finish.

Instead of describing how or what was stored by each agent, and how, in what format, how it was selected, the whole process is described as Claude-generated workflows and pipelines, which brings 0 value.

Are we reading the same website? The predictions for China are completely fantastical - they are not falling behind and if anything the gap is narrowing, and they aren't stealing the weights because they don't need to (distilling is enough). Also the CDZ stuff is just fanfiction at this point.

Everything starting from mid-2026 is incredibly wrong, which is pretty poor predictive performance considering they published it mid-2025. List of misses:

- In mid-2026, China will be six months behind the released models of the American labs. (In mid-2026, China released Kimi K3, which was equivalent to the best released U.S. model at the time, Claude Fable.)

- In mid-2026, China will nationalize all domestic AI labs and centralize them into a new special economic zone, the "CDZ," created around a nuclear reactor. 80% of all chips in China will be redirected to the CDZ. This uh. Obviously didn't happen.

- In late 2026 OpenAI/Anthropic will release a tiny model that outperforms their previously-best 2026 model (Astra/Fable), that is priced 10x cheaper, and they will market fine-tuning for it. (Lol.)

- AI will increase the stock market by late 2026 by 30% (in fact the S&P 500 is up 13.9% YTD, which is pretty good, but not at all out of the ordinary).

Their predictions for 2027 also seem pretty wrong: e.g. they expect continual learning to be solved in January, and RSI including from a research perspective, not just "optimize CUDA kernels," to also be solved in January, which I suspect no serious lab employee believes anymore. Most lab employees believe RSI will happen (as do I!), of course, but not in two and a half months.

By early 2027 they expect cyberwar with China because China has stolen the weights of a model and smuggled it into the CDZ.

By mid-2027 they expect true superintelligence, where essentially all engineers and researchers are useless, unable to comprehend the advances the AI is able to make and largely unable to even usefully manage or steer AI, who are better at self-managing than a human would be.

Considering their 2025-mid-2026 predictions were basically "progress continues as normal and nothing crazy happens," it's not super surprising that they were more or less right about that. Their 2026/2027 predictions were the spicy ones, and none of them have happened, or are even on track to happening!

Probably a bit ahead of it.

If one believes Zitron / Microsoft claims that like ~50% of AI chips bought are in warehouses, and current projected DC delays... the TLDR is the amount of compute lead (aka what drives lead) is fraction of projection, which will shift timelines accordingly. And really if DC build out delays is due to infra or scrubbed altogether, we're talking about years.

Pretty amazingly accurate

Did you read it to the end? It is insane speculative fiction. They are not even qualitatively right about one of the main narrative planks, which is that by now (mid-late 2026) there would be one clearly dominant company each in the U.S. and China, and that the latter would be nationalizing their compute.

Damn this is terrifying, I kinda wish I hadn't read this

I’m still putting it at 2029 for real-world work acceleration.

Yep we're basically inventing all of the RSI loops that AI 2027 thought we'd invent lol.