ByteDance, Tsinghua, and Shanghai AI Lab jointly propose Direct On-Policy Distillation (Direct-OPD) in arXiv 2607.05394, solving the puzzle of RLVR's "post-training is the new scaling bottleneck". The core idea isn't to distill the weak teacher's final policy, but to distill the log-ratio of its two checkpoints before and after RL, using it as the implicit reward for a strong student on its own on-policy state — thereby zero-cost migrating the RL supervisory signal that ran on the weak model to the strong model. Results: Qwen3-1.7B on AIME 2024 jumps from 48.3% to 58.3%, using only 8 A100s and 4 hours, and step-matched consistently beats direct RL. Going further, the "policy difference" can be sequentially stacked onto the same student — that is, multiple weak models' RL experience can incrementally accumulate like LoRA, shifting the post-training path from "retrain each large model" to "stack small-model experience". This paradigm forms a strong complement to the recent RL post-training wave from teams like Qwen and Cognition's Kimi, and is worth tracking for reuse in the GPT / Claude post-training stacks.