It addresses post-training bottlenecks like complex YAML configurations.
Proper post-training RL, deployed broadly, is a key step towards a future where software systems quietly improve themselves and adapt to human needs over time.
It is currently blocked on infrastructure -- tangled in spaghetti code and shackled by YAML.
We're working to fix it.
RL these days generally requires three things: inference, training, and sandboxes. I wonder what AI infrastructure provider has all three?
It addresses post-training bottlenecks like complex YAML configurations.
Proper post-training RL, deployed broadly, is a key step towards a future where software systems quietly improve themselves and adapt to human needs over time.
It is currently blocked on infrastructure -- tangled in spaghetti code and shackled by YAML.
We're working to fix it.
Users express optimism that reinforcement learning infrastructure can improve after Modal released its open-source library for scaling RL training.
