  We propose here some new sampling algorithms for Path Sampling in the case
when stochastic dynamics are used. In particular, we present a new proposal
function for equilibrium sampling of paths with a Monte-Carlo dynamics (the
so-called ``brownian tube'' proposal). This proposal is based on the continuity
of the dynamics with respect to the random forcing, and generalizes all
previous approaches. The efficiency of this proposal is demonstrated using some
measure of decorrelation in path space. We also discuss a switching strategy
that allows to transform ensemble of paths at a finite rate while remaining at
equilibrium, in contrast with the usual Jarzynski like switching. This
switching is very interesting to sample constrained paths starting from
unconstrained paths, or to perform simulated annealing in a rigorous way.
