Make docstring of ForwardMode more precise, add references to it wher…

…e forward-mode autodiff is mentioned in the other adjoints

diffrax/_adjoint.py

@@ -184,7 +184,9 @@ class RecursiveCheckpointAdjoint(AbstractAdjoint):
     !!! info
 
         Note that this cannot be forward-mode autodifferentiated. (E.g. using
-        `jax.jvp`.) Try using [`diffrax.DirectAdjoint`][] if that is something you need.
+        `jax.jvp`.) Try using [`diffrax.DirectAdjoint`][] if you need both forward-mode
+        and reverse-mode autodifferentiation, and [`diffrax.ForwardMode`][] if you need
+        only forward-mode autodifferentiation.
 
     ??? cite "References"
 
@@ -333,6 +335,8 @@ class DirectAdjoint(AbstractAdjoint):
 
     So unless you need forward-mode autodifferentiation then
     [`diffrax.RecursiveCheckpointAdjoint`][] should be preferred.
+    If you need only forward-mode autodifferentiation, then [`diffrax.ForwardMode`][] is
+    more efficient.
     """
 
     def loop(
@@ -855,11 +859,13 @@ def loop(
 
 
 class ForwardMode(AbstractAdjoint):
-    """Differentiate through a differential equation solve during the forward pass.
-    (So it is not really an adjoint - it is a different way of quantifying the
-    sensitivity of the output to the input.)
+    """Supports forward-mode automatic differentiation through a differential equation
+    solve. This works by propagating the derivatives during the forward-pass - that is,
+    during the ODE solve, instead of solving the adjoint equations afterwards.
+    (So this is really a different way of quantifying the sensitivity of the output to
+    the input, even if its interface is that of an adjoint for convenience.)
 
-    ForwardMode is useful when we have many more outputs than inputs to a function - for
+    This is useful when we have many more outputs than inputs to a function - for
     instance during parameter inference for ODE models with least-squares solvers such
     as `optimistix.Levenberg-Marquardt`, that operate on the residuals.
     """