Show that if $\mathcal{H}$ is PAC learnable in the standard one-oracle model, then $\mathcal{H}$ is PAC learnable in the two-oracle model
Recall the following lemma. Let $X,Y$ be two random variables satisfying $\Pr[X\le\epsilon]\ge 1-\delta$ and $\Pr[Y\le\epsilon]\ge 1-\delta$ then $\Pr[X\le \epsilon \land Y\le \epsilon]\ge 1-2\delta$.
Suppose $\mathcal{H}$ is PAC learnable (realizable and proper). Let $m(\epsilon,\delta)$ be the guaranteed PAC sample complexity. Given $m(\epsilon,\delta/2)$ samples from $D^+$ and $m(\epsilon,\delta/2)$ samples from $D^-$, find $h_{ERM}$ that is consistent with all samples (both positive and negative). Since $\mathcal{H}$ is PAC learnable, we know that
$\Pr[L_{D^+}(h_{ERM})\le\epsilon], \hspace{1mm} \Pr[L_{D^-}(h_{ERM})\le\epsilon]\ge 1-\delta/2$. Note that we strongly used the fact that the PAC bound is achieved by any sample consistent hypothesis. Now Use the above lemma to show that the overall empirical risk minimizer is a 2-oracle PAC learner for $\mathcal{H}$.