Understanding REINFORCE loss
I think the intuition here is that you want to push the negative loglikelihood (NLL) * the reward to be as negative as possible. Since our reward is often not differentiable because it is obtained through sampling, we can only change the NLL. For actions with high reward, we have greater &quot;pressure&quot; / gradient to push the NLL down, than if the reward was low, as the reward acts as a multiplier on the gradient on the NLL. So you'd imagine at convergence/equilibrium, the high reward actions would have greater likelihood than the low reward ones. Let me know if that makes sense!