AI trains AI already, agents are happy to spin up real training pipelines for deep learning or regression models or whatever you want right? I guess the advantage to your project is that it provides a framework to allow the agent to access extra compute?
Yes I'd heard the labs (Anthropic mostly) speaking about LLMs training LLMs, so I wanted to make things a little more concrete and test it out myself! Essentially you are correct though, my framework allows the agent access to compute, but also the agent itself is being trained to become better at training models with that compute.
“The Anthropic team for their incredible coding models (Fable-5 wrote every line of code in this project), and the Claude Code harness.”
Source: the repo
When you vibe code a system in a complex area like RL, you basically have zero understanding of what its actually doing, whether its actually any good or not, what you're actually benchmarking, and when the system would fail.
I chose the key technical decision and direction (such as the system architecture, the tasks to train on, the stack of Tinker, Prime-RL & Runpod - all of which I know well) etc.
The problems it would do well on are training small agentic (multi-turn, tool use) task based models using the prime-rl stack, which are close to the distribution trained upon. It would likely not transfer to other training frameworks such as SLIME, ART or ROLL, it would also likely not transfer well to RL for complex agents such as coding agents etc.
It is limited due to its scale. As a single person, the resources required to train this on a more diverse dataset, with more complex tasks on a larger variety of models, is outside my abilities! I believe there are many avenues to explore to improve performance for this to be genuinely valuable. For now, this just a proof of concept to show the possible.
I would like to think I have a good understanding of RL, evaluations, and agentic systems after a few years of working on these areas. However, I will always have gaps. I use Fable to help accelerate me, and fill those gaps at the same time, from which I can learn from too.
I think the counter point for these projects is that you may not need a deep understanding if you can measure the outcome. While this may not be true every time today, it plausibly will be in the future - making the activity worthwhile.
Yes I do agree with this. I believe we are shifting from "make the model good" (prompt/context engineering, etc) to "define good for the model" (success criteria/rubrics). Over time I believe this will become increasingly obvious (as long as model capabilities continue to increase).
Well, you say that, but when "measuring" anything in RL, that measurement itself is not always obvious.
That is, creating the scoring system/judge models etc for RL is not easy at all. You can easily create an RL loop which is getting better and improving its scores, but actually the result is totally garbage, because you're measuring the wrong thing.
AI trains AI already, agents are happy to spin up real training pipelines for deep learning or regression models or whatever you want right? I guess the advantage to your project is that it provides a framework to allow the agent to access extra compute?
Yes I'd heard the labs (Anthropic mostly) speaking about LLMs training LLMs, so I wanted to make things a little more concrete and test it out myself! Essentially you are correct though, my framework allows the agent access to compute, but also the agent itself is being trained to become better at training models with that compute.
Lots of emoji in that readme. Was it mainly codex?
“The Anthropic team for their incredible coding models (Fable-5 wrote every line of code in this project), and the Claude Code harness.” Source: the repo
Mainly Fable, but It was me who wanted to emojis added hah. I also of course edited the README by hand (crazy I know), but the code is entirely fable
What is RL?
Reinforcement Learning.
No idea why you got downvoted into oblivion with the context post. Cool idea!
Thank you!
Can you explain how it works?
What problems would it do well on and why?
Where would it start to fail/break?
What are the limitations of a system like this?
When you vibe code a system in a complex area like RL, you basically have zero understanding of what its actually doing, whether its actually any good or not, what you're actually benchmarking, and when the system would fail.
It's the blind leading the blind.
I chose the key technical decision and direction (such as the system architecture, the tasks to train on, the stack of Tinker, Prime-RL & Runpod - all of which I know well) etc.
The problems it would do well on are training small agentic (multi-turn, tool use) task based models using the prime-rl stack, which are close to the distribution trained upon. It would likely not transfer to other training frameworks such as SLIME, ART or ROLL, it would also likely not transfer well to RL for complex agents such as coding agents etc.
It is limited due to its scale. As a single person, the resources required to train this on a more diverse dataset, with more complex tasks on a larger variety of models, is outside my abilities! I believe there are many avenues to explore to improve performance for this to be genuinely valuable. For now, this just a proof of concept to show the possible.
I would like to think I have a good understanding of RL, evaluations, and agentic systems after a few years of working on these areas. However, I will always have gaps. I use Fable to help accelerate me, and fill those gaps at the same time, from which I can learn from too.
I think the counter point for these projects is that you may not need a deep understanding if you can measure the outcome. While this may not be true every time today, it plausibly will be in the future - making the activity worthwhile.
Yes I do agree with this. I believe we are shifting from "make the model good" (prompt/context engineering, etc) to "define good for the model" (success criteria/rubrics). Over time I believe this will become increasingly obvious (as long as model capabilities continue to increase).
Well, you say that, but when "measuring" anything in RL, that measurement itself is not always obvious.
That is, creating the scoring system/judge models etc for RL is not easy at all. You can easily create an RL loop which is getting better and improving its scores, but actually the result is totally garbage, because you're measuring the wrong thing.
Did you read the README?
The AI generated README?