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.
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.
I could make this exact same thing over a weekend and post it on Hackernews. But I won't because I would be embarrassed to do so.
The bar for posting something to HN should be high, the bar for wanting people to read your code, your writing, should be putting serious effort and thought into it. Not just vibe coding something up with a vibe coded README and 100% vibe coded code and not even a novel idea or implementation.
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.