Well, duh. If you could do this with Opus 4.8, we would know. When Astra’s successor is 2-3x better at math research, and the internal teams say “we believe we will get there,” I’m inclined to believe the insiders.
Yeah, it’s crazy how fast things have changed in a month. I couldn’t find a more recent replication or similar study but it would be interesting to see it done with the current frontiers. Though I don’t think that would change much about the overall conclusion of the paper
The actual title of the paper is: "Can AI agents conduct open-ended AI research? Early evidence from two case studies"
While I appreciate that the article is throwing a web blanket on doomer claims, the actual study doesn't really get into AI self-improvement. That doesn't require writing papers. That just requires autonomously writing a software system that can produce a better AI agent then the one that created it. That said, I have little worry about this being possible as I have seen no evidence of AI agents being able to produce a working software system of that scale.
I don't think RSI is typically used to describe self-improving agents - it's about improving the model itself, and its performance in agentic tasks.
Most of the gains in model performance from one release to the next are coming from RLVR post training, which has changed a lot over the last couple of years.
The old way was the model generates a response, then a static verifier looks at the response and evaluates it to assign a reward score. The new way is interactive with an agent running in a custom RL task simulation environment, then scored according to how well it completed the assigned task. For a SOTA model there will be many thousands of these simulation environments, each focusing on trying to teach the model/agent a different skill. Post-training also typically uses training curricula to walk the model up though through different levels of complexity.
Training has become very complex.
The job of a post-training AI research engineer consists of things like designing environments, designing training curricula, tweaking learning algorithms, running small scale experiments to verify ideas, etc.
When people talk about RSI, it seems what they are talking about is really automating the job of the post-training research engineer - coming up with new ideas, testing them out, building these environments, etc. At the end of the day there is only so much speed-up to be had since in the end you still need to actually run those experiments and do the post-training, and are bottle-necked by the amount of compute available to do this.
It's not all-or-nothing since some aspects of this automating the job of the post-training research engineer are easier than others, and are already being done, while the job as a whole obviously requires full human intelligence.
There’s also the difference between a model recursively improving “itself” and improving itself via online learning.
The former being that these models are helping develop and train future models, but they might not veer too far off in architecture (yet).
The latter is a model being able to train/learn on the fly, in real time, permanently (not just in the current conversation/session), or in other words, adjusting/managing its own weights. But, it also seems like it would take an entire paradigm shift in model architecture from what most LLMs are built on, but I could be wrong.
Of course it might not, it has been the holy grail of AI research for a long time. It would be great if we could leave some self improving code running on a blank slate of a computer while we sleep and the machine was crying asking me what is everything the next morning. None of AI researchers have had that moment outside of their dreams, so far, but it would be great if it happened.
this article reads like a joke the "new study" is from group of people that are not at the frontier. they test with $3k of anthropic credits (compare to the >$10M in compute used to solve recent NS last week)
The method of the NS advance involved RLHE (reinforcement learning via human example), and that is only open-ended if users continue to advance the frontier within chats ahead of publications.
Sure, but the point is that the labs use more powerful internal models for research work, not public models. Public models tend to lag the internal frontier by a decent margin, and are constrained in other ways by monitoring. It’s just not a useful indicator.
Well, duh. If you could do this with Opus 4.8, we would know. When Astra’s successor is 2-3x better at math research, and the internal teams say “we believe we will get there,” I’m inclined to believe the insiders.
Opus 4.8 plus OpenClaw. I feel like the space is moving so fast that the result with this setup says very little about how close we are actually now.
Yeah, it’s crazy how fast things have changed in a month. I couldn’t find a more recent replication or similar study but it would be interesting to see it done with the current frontiers. Though I don’t think that would change much about the overall conclusion of the paper
Link to the actual paper: https://arxiv.org/abs/2607.27191
The actual title of the paper is: "Can AI agents conduct open-ended AI research? Early evidence from two case studies"
While I appreciate that the article is throwing a web blanket on doomer claims, the actual study doesn't really get into AI self-improvement. That doesn't require writing papers. That just requires autonomously writing a software system that can produce a better AI agent then the one that created it. That said, I have little worry about this being possible as I have seen no evidence of AI agents being able to produce a working software system of that scale.
I don't think RSI is typically used to describe self-improving agents - it's about improving the model itself, and its performance in agentic tasks.
Most of the gains in model performance from one release to the next are coming from RLVR post training, which has changed a lot over the last couple of years.
The old way was the model generates a response, then a static verifier looks at the response and evaluates it to assign a reward score. The new way is interactive with an agent running in a custom RL task simulation environment, then scored according to how well it completed the assigned task. For a SOTA model there will be many thousands of these simulation environments, each focusing on trying to teach the model/agent a different skill. Post-training also typically uses training curricula to walk the model up though through different levels of complexity.
Training has become very complex.
The job of a post-training AI research engineer consists of things like designing environments, designing training curricula, tweaking learning algorithms, running small scale experiments to verify ideas, etc.
When people talk about RSI, it seems what they are talking about is really automating the job of the post-training research engineer - coming up with new ideas, testing them out, building these environments, etc. At the end of the day there is only so much speed-up to be had since in the end you still need to actually run those experiments and do the post-training, and are bottle-necked by the amount of compute available to do this.
It's not all-or-nothing since some aspects of this automating the job of the post-training research engineer are easier than others, and are already being done, while the job as a whole obviously requires full human intelligence.
Do you think the final product of research is papers?
There’s also the difference between a model recursively improving “itself” and improving itself via online learning.
The former being that these models are helping develop and train future models, but they might not veer too far off in architecture (yet).
The latter is a model being able to train/learn on the fly, in real time, permanently (not just in the current conversation/session), or in other words, adjusting/managing its own weights. But, it also seems like it would take an entire paradigm shift in model architecture from what most LLMs are built on, but I could be wrong.
Of course it might not, it has been the holy grail of AI research for a long time. It would be great if we could leave some self improving code running on a blank slate of a computer while we sleep and the machine was crying asking me what is everything the next morning. None of AI researchers have had that moment outside of their dreams, so far, but it would be great if it happened.
this article reads like a joke the "new study" is from group of people that are not at the frontier. they test with $3k of anthropic credits (compare to the >$10M in compute used to solve recent NS last week)
> The researchers asked Anthropic’s Claude Opus 4.8, running on open-source software called OpenClaw
Meanwhile, Navier–Stokes was solved by an internal model significantly more capable than Astra (and therefore more capable than Mythos/Fable).
I’m afraid this sort of experiment is cope. The labs clearly believe RSI is coming soon.
The method of the NS advance involved RLHE (reinforcement learning via human example), and that is only open-ended if users continue to advance the frontier within chats ahead of publications.
Sure, but the point is that the labs use more powerful internal models for research work, not public models. Public models tend to lag the internal frontier by a decent margin, and are constrained in other ways by monitoring. It’s just not a useful indicator.