Self-scaffolding RL is a reinforcement-learning training method where the policy model does three things per training cycle instead of just solving fixed problems: it proposes a new task based on its own solving history, builds or refines a task-specific scaffold (instructions, tools, decomposition and orchestration strategy) for solving it, and produces a solution rollout under that scaffold. Reward from the rollout propagates back across all three stages via a method like GRPO, so the model learns to generate better tasks and better scaffolds, not just better solutions. Task reward typically combines validity (a hard gate — invalid tasks score zero), frontier difficulty (reward peaks near a target success rate, pushing the generator toward harder problems as the model improves), and novelty (a secondary penalty against near-duplicate tasks). Ornith AI popularized the term with Ornith-1.0's scaffold-only version and Ornith-1.5's full task-plus-scaffold loop.