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.

Did you read the README?