Jiahua Bao

Chasing large token-level teacher-student gaps in agent distillation is largely misguided: local divergence fails to predict task completion, whereas unpenalized minor deviations often trigger catastrophic multi-turn failure.

Sparse rewards can be effectively optimized without losing the advantages of fine-grained feedback through a novel two-stage training approach.