Their paper https://mistral.ai/static/research/magistral.pdf is also cool! They edited GRPO via:
1. Removed KL Divergence
2. Normalize by total length (Dr. GRPO style)
3. Minibatch normalization for advantages
4. Relaxing trust region
Does anyone know why they added minibatch advantage normalization (or when it can be useful)?
The paper they cite "What matters in on-policy RL" claims it does not lead to much difference on their suite of test problems, and (mean-of-minibatch)-normalization doesn't seem theoretically motivated for convergence to the optimal policy?
Tbh I'm unsure as well I took a skim of the paper so if I find anything I'll post it here!
> Removed KL Divergence
Wait, how are they computing the loss?
Oh it's the KL term sorry - beta * KL ie they set beta to 0.
The goal of it was to "force" the model not to stray to far away from the original checkpoint, but it can hinder the model from learning new things
It's become trendy to delete it. I say trendy because many papers delete it without offering any proof that it is meaningless