DASH Boosts Reasoning Models by Adapting Self‑Distillation to Divergence
Traditional on‑policy self‑distillation treats every teacher‑student divergence equally, wasting temporal context. DASH introduces divergence‑adaptive gates that modulate token‑level supervision based on how the gap evolves, letting the model propagate dense signals more effectively. Tested on three math reasoning suites, it lifts performance at every model scale without extra forward passes.