new blogpost after a long time! in this series i will talk about how to solve reinforcement learning for long-horizon tasks, incrementally from the most straightforward approaches. (link in replies!) in part I of this series, we throw RL at the cube in its most direct, unvarnished form and weaponize failure itself. the goal of this blog is to watch the RL footguns fire in slow motion and see how reward sparsity turns into a policy collapse nightmare, why exploration can suffocate in long-horizon spaces, and what happens behind the scenes when a model sounds confident while remaining fundamentally lost! special thanks to @willccbb and @PrimeIntellect for sponsoring this :) verifiers is an incredible tool and i wish them the best.