Before revision:  Both algorithms are based on an optimistic least-squares version of value iteration analogous to the finite-horizon backward induction approach from Jin et al. 2020. To the best of our knowledge, these are the first regret bounds for stochastic shortest path that are independent of the size of the state and action spaces .
After revision:  Both algorithms are based on an optimistic least-squares version of value iteration analogous to the first for SSP with function approximation .