Played around with this. This was exactly something I was looking for! Tried a few things - Creating an env - pretty dope! end to end claude was able to port it from github with only minor issues. One shotted @ShashwatGoel7 OpenForecaster env here. A lot more people should contribute their own envs. I hope they launch monetisation here. Running a curator over env tasks during RL - When there are so many tasks, which one should you focus on? This is the auto-curriculum/meta-learning bit. I am still not able to beat random/pass@k but I think signals are there over long run this will help with diversity. This obviously has a power law, every run will have top envs dominating but I feel those 20% random tasks will give a big boost to any model. optimise the GEPA optimiser - gepa is great but pretty slow. What if we could teach a model to do this better? This was in my list for so long, finally with openreward was able to attempt it.
