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Improving Reasoning Ability via Asynchronous On-Policy Self-Distillation under Positive Rollouts
Distillation and reinforcement learning through verifiable rewards (RLVR) have achieved progress in enhancing the reasoning ability of large language models (LLMs). However, we note that negative rollouts may admit no gradation of failure severity, and the combinatorial vastness makes penalizing a f
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