The primary goal of the release and our notebook https://colab.research.google.com/github/unslothai/notebooks... was actually to showcase how to mitigate reward hacking in reinforcement learning - for example when RL learns to cheat and output global variables instead like editing the timer to cheat on benchmarking and others. You can edit the notebook to do rl on other powerful models like Qwen, Llama etc automatically with Unsloth as well via our automatic compiler! We also made sink attention and moe inference super optimized for training - note flash attention 3 doesn't have sink backwards support so you'll have to use unsloth.

Gpt-oss tbh in our tests is a truly powerful model, especially the 120b variant - it's extremely popular in western enterprises since yes it's from openai but also because reasoning mode high and the censored nature and its reasoning capabilities are attractive. A big underutilized feature is its web search and internal intermediate tool calling which it can do as part of its reasoning chain just like o3 or gpt5.

RL yes isn't an all powerful hammer, but it can solve so many more new problems. For a financial institution, you can make automatic trading strategies via RL. For an intelligence agency, decryption via RL. For a legal startup, possibly case breakthroughs via RL, automatic drug candidates etc. And yes, big labs want to automate all tasks via massive RL for eg being able to play pokemon and all other games as one example. RL opened so many doors since you don't need any data, just one prompt like "make fast matrix multiplications kernels", and reward functions - it can allow many more interesting use cases where data is a constraint!