R2S Fixes Myopic Teacher Scoring in Agentic On-Policy Distillation
The non-obvious insight is that the failure of agentic distillation comes from the scorer's myopia, not from students drifting out of the teacher's reliable region, so the fix targets the teacher instead of constraining rollouts. Builders training agents via distillation can adopt R2S without task reward labels, using consensus or an LLM judge as the check. Code and details are on the project page at tree-shu-zhao.github.io/r2s/.