diff --git a/src/default.jl b/src/default.jl
index 758e049f3..8ce34ec0d 100644
--- a/src/default.jl
+++ b/src/default.jl
@@ -252,12 +252,21 @@ end
 
 ## Defaults
 
-function SciMLBase.__init(prob::NonlinearProblem{uType, iip}, alg::Nothing, args...;
-        kwargs...) where {uType, iip}
-    SciMLBase.__init(prob, FastShortcutNonlinearPolyalg(), args...; kwargs...)
+function SciMLBase.__init(prob::NonlinearProblem, ::Nothing, args...; kwargs...)
+    return SciMLBase.__init(prob, FastShortcutNonlinearPolyalg(), args...; kwargs...)
 end
 
-function SciMLBase.__solve(prob::NonlinearProblem{uType, iip}, alg::Nothing, args...;
-        kwargs...) where {uType, iip}
-    SciMLBase.__solve(prob, FastShortcutNonlinearPolyalg(), args...; kwargs...)
+function SciMLBase.__solve(prob::NonlinearProblem, ::Nothing, args...; kwargs...)
+    return SciMLBase.__solve(prob, FastShortcutNonlinearPolyalg(), args...; kwargs...)
+end
+
+# FIXME: We default to using LM currently. But once we have line searches for GN implemented
+#        we should default to a polyalgorithm.
+function SciMLBase.__init(prob::NonlinearLeastSquaresProblem, ::Nothing, args...; kwargs...)
+    return SciMLBase.__init(prob, LevenbergMarquardt(), args...; kwargs...)
+end
+
+function SciMLBase.__solve(prob::NonlinearLeastSquaresProblem, ::Nothing, args...;
+        kwargs...)
+    return SciMLBase.__solve(prob, LevenbergMarquardt(), args...; kwargs...)
 end
diff --git a/test/nonlinear_least_squares.jl b/test/nonlinear_least_squares.jl
index 07a310196..5b4ff265e 100644
--- a/test/nonlinear_least_squares.jl
+++ b/test/nonlinear_least_squares.jl
@@ -36,6 +36,7 @@ append!(solvers,
         LevenbergMarquardt(; linsolve = LUFactorization()),
         LeastSquaresOptimJL(:lm),
         LeastSquaresOptimJL(:dogleg),
+	nothing,
     ])
 
 for prob in nlls_problems, solver in solvers