Rethinking OPD: One Query, Hundreds of Steps |Bingxiang He Why can a single query keep improving a model for hundreds of training steps? Join Bingxiang He (@HBX_hbx) for a deep dive into Rethinking OPD and One-Shot OPD, hosted by @qingke_ai and simulcast on Zhihu Frontier. 📅 Sept 17 · 8 PM Beijing / 12 PM UTC 📺 Watch live:

A nice theoretical revisit of OPD. With the same supervision budget, supervising only the first 30% of tokens nearly matches standard OPD, while supervising only the last 30% barely works. Framing OPD as a constrained optimization problem naturally leads to an importance-weighted objective, resulting in IW-OPD, which improves sample efficiency and performance with no extra inference cost. Paper: Blog: