Let $\tau$ be a Markov time, and define the usual $\sigma$-algebras: $$\mathcal F^{<\tau} = \sigma\{X_t^{-1}(E) \cap \{t < \tau\} : t \geq 0, \, E \text{ — Borel}\}$$ and $$\begin{aligned} \mathcal F_{\geqslant\tau} & = \sigma\{X_{\tau + t}^{-1}(E) : t \geq 0, \, E \text{ — Borel}\} \\ & = \sigma\{X_t^{-1}(E) \cap \{t \geqslant \tau\} : t \geq 0, \, E \text{ — Borel}\} . \end{aligned} $$ (There are some regularity issues involved in the last equality, but let us ignore them.) Strong Markov property asserts that $\mathcal F^{<\tau}$ and $\mathcal F_{\geqslant \tau}$ are conditionally independent, given $\sigma\{X_\tau\}$.

Let $0 = \tau_0 < \tau_1 < \tau_2 < \ldots < \tau_n < \tau_{n+1} = \infty$ (compared to the original question, the order of $\tau_j$ is reversed here). Define in a similar way
$$ \mathcal F_j := \mathcal F_{\geqslant \tau_{j-1}}^{<\tau_j} = \sigma\{X_t^{-1}(E) \cap \{\tau_{j-1} \leqslant t < \tau_j\} : t \geq 0, \, E \text{ — Borel}\}$$
By the strong Markov property, $\bigvee_{j \leqslant k} \mathcal F_j$ and $\bigvee_{j > k} \mathcal F_j$ are conditionally independent, given $\sigma\{X_{\tau_k}\}$ — and therefore also given a larger $\sigma$-algebra $\mathcal G := \sigma\{X_{\tau_j} : j = 1, 2, \ldots, n\}$.

The above property implies that the family of $\sigma$-algebras $\mathcal F_1, \mathcal F_2, \ldots, \mathcal F_n, \mathcal F_{n+1}$ is conditionally independent, given $\mathcal G$ (a rigorous argument is somewhat tiresome, though). It remains to note that the random variable $R_j = \int_{\tau_{j-1}}^{\tau_j} \phi(X_s) ds$ for an arbitrary numerical function $\phi$ is measurable with respect to $\mathcal F_j$ (these random variables correspond to the random variables $R_j$ in the question, in a reversed order).