A survey of on-policy self distillation approaches. 1/ The field can be summarized succinctly as: use log π(y | x, c) - log π(y | x) in some way as training signal. Most variants just differ on how c is derived, ranging from putting the literal answer in context (OPSD/SDFT) to writing a new skill and putting that into context (SEED). 2/ I haven't seen any papers yet proving this works on production-grade settings (e.g, thinking models in agentic settings). Worse, evidence shows that pure OPSD outright degrades thinking model performance ( This matches my lived experience: pure OPSD worked on initial training steps, but quickly degrades the model on longer runs. So where does that leave us? OPSD / SDFT gives degenerate signal, and approaches that try to fix the signal with extra information (RLCSD, purified OPSD) aren't simple enough to be a good primitive. IMO the two promising directions are one of: - Limit OPSD signal to just local credit assignment inside normal RL, as in Composer 2.5 training. - Use environmental feedback to write a targeted system prompt for the teacher. This feels like the best path to getting non-degenerate signal.