# Reward-Aligned Distillation

Distillation no longer treats every teacher token alike: each token's weight is set by its predicted effect on the student's post-update reward. Earlier token-selection methods relied on proxies such as uncertainty or teacher-student divergence, which need not identify reward-improving supervision. RAD scores each token by differentiating through a temporary optimizer step, then updates token weights by projected ascent, improving mean accuracy on six competition-math benchmarks by 6.5 points for Qwen3-1.7B and 2.5 points for Qwen3-4B-Instruct-2507 at a matched training-response budget, and reaching 54.69% and 58.67% pass@1 on LiveCodeBench-v6.

Distillation no longer treats every teacher token alike: each token's weight is set by its predicted effect on the student's post-update reward. Earlier token-selection methods relied on proxies such as uncertainty or teacher-student divergence, which need not identify reward-improving supervision. RAD scores each token by differentiating through a temporary optimizer step, then updates token weights by projected ascent, improving mean accuracy on six competition-math benchmarks by 6.5 points for Qwen3-1.7B and 2.5 points for Qwen3-4B-Instruct-2507 at a matched training-response budget, and reaching 54.69% and 58.67% pass@1 on LiveCodeBench-v6.