Yiming Ma retweeted
As the corresponding author, I'd like to provide an important update regarding CUDA Agent:
After our March arXiv preprint, the Chinese CUDA/AI community identified a small number of reward hacking behaviors in the benchmarking of our CUDA Agent (see Zhihu post below). We have since taken the time to carefully fix the reward hacking issues, and are now excited to share a new updated and improved CUDA Agent! We will be releasing the updated paper soon.
Despite the unexpected challenge, this experience shows the importance of attention to detail in agentic RL. You end up discovering all kinds of interesting and quirky reward hacks when you go digging in the weeds. It also demonstrates that investing time and effort into building a set of robust evals should always be one of the top priorities of agentic RL.
More updates on the paper will be provided in the coming weeks.
ByteDance just published something I've been waiting for someone to build: CUDA Agent!
It trained a model that writes fast CUDA kernels. Not just correct ones — actually optimized ones.
It beats torch.compile by 2× on simple/medium kernels, ~92% on complex ones, and even outperforms Claude Opus 4.5 and Gemini 3 Pro by ~40% on the hardest setting.
The key idea is simple but kind of brilliant:
CUDA performance isn’t about correctness, it’s about hardware. Warps, memory bandwidth, bank conflicts — the stuff you only see in a profiler.
So instead of rewarding “did it compile?”, they reward actual GPU speed. Real profiling numbers. RL trained directly on performance.
That’s a big shift.
Paper: arxiv.org/abs/2602.24286
Project: cuda-agent.github.io/
