Recently, reinforcement learning (RL) has attracted substantial research interests. Much of the attention and success, however, has been for the discrete time setting. Continuous-time RL, despite its natural analytical connection to stochastic controls, has been largely unexplored and with limited progress. In particular, characterizing sample efficiency for continuous-time RL algorithms with convergence rate remains a challenging and open problem. In this talk, we will discuss some recent advances in the convergence rate analysis for the episodic linear-convex RL problem, and report a regret bound of the order $O(\sqrt{N \ln N})$ for the greedy least-squares algorithm, with $N$ the number of episodes. The approach is probabilistic, involving establishing the stability of the associated forward-backward stochastic differential equation, studying the Lipschitz stability of feedback controls, and exploring the concentration properties of sub-Weibull random variables. In the special case of the linear-quadratic RL problem, the analysis reduces to the regularity and robustness of the associated Riccati equation and the sub-exponential properties of continuous-time least-squares estimators, which leads to a logarithmic regret.