What does this equation mean?

Read the inner sum literally: every action in the trajectory is credited with everything that happens from that point on, not with its own specific contribution. When T is small and rewards are dense, that crude attribution washes out quickly. When T is large and the reward is a single terminal signal — a multi-step coding task that either compiles and passes its tests or does not, a multi-turn support conversation that either resolves the case or does not — the same sum assigns identical credit to a decisive action, a neutral one, and a mistake that happened to be recovered from later. The variance of the resulting gradient estimate grows with the length of the trajectory, and no architectural trick changes that arithmetic; it only changes how the trajectory is chunked before credit is assigned.