New Salesforce paper finds that distilling from a big external teacher usually costs more compute than plain RL, while merging RL-trained domain experts saves 42%.

On-policy distillation lets a student learn from a teacher's per-token feedback on its own outputs, and many expect this dense signal to beat RL. But every training token also needs a teacher forward pass.

They compared distillation and GRPO by held-out accuracy per training FLOP, varying task difficulty, model sizes, domains, and training settings.

With an external post-trained teacher, GRPO generally won, and distillation often inflated response length. Distilling per-domain GRPO experts into 1 student matched joint GRPO on math plus code with 42.0% fewer total FLOPs.

To save compute, train per-domain experts with RL and distill them into 1 model, rather than distilling from a big external teacher.