diff --git a/docs/src/models/recurrence.md b/docs/src/models/recurrence.md
index 7c20165d..8030f4c4 100644
--- a/docs/src/models/recurrence.md
+++ b/docs/src/models/recurrence.md
@@ -114,3 +114,13 @@ truncate!(m)
 Calling `truncate!` wipes the slate clean, so we can call the model with more inputs without building up an expensive gradient computation.
 
 `truncate!` makes sense when you are working with multiple chunks of a large sequence, but we may also want to work with a set of independent sequences. In this case the hidden state should be completely reset to its original value, throwing away any accumulated information. `reset!` does this for you.
+
+In general, when training with recurrent layers in your model, you'll want to call `reset!` or `truncate!` for each loss calculation:
+
+```julia
+function loss(x,y)
+  l = Flux.mse(m(x), y)
+  Flux.reset!(m)
+  return l
+end
+```