EP351: Direct-OPD slashes AI reasoning compute costs

Title: Weak-to-Strong Generalization via Direct On-Policy DistillationSource: http://arxiv.org/abs/2607.05394v1Summary:This work introduces a novel post-training paradigm that transfers the reinforcement learning policy shift of a smaller, cheaper weak model as an implicit reward signal to a stronger target model. By bypassing the need for explicit reward modeling or expensive on-policy rollouts on scaled architectures, it represents a major efficiency breakthrough for training high-capability reasoning models.