diff --git a/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py b/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py
index e8d86e8b56cd2f..d51c86539007fc 100644
--- a/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py
+++ b/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py
@@ -24,7 +24,7 @@
 
 X_DIM = 3
 U_DIM = 1
-PARAM_DIM= 4
+PARAM_DIM = 4
 COST_E_DIM = 5
 COST_DIM = COST_E_DIM + 1
 CONSTR_DIM = 4
@@ -236,6 +236,7 @@ def set_weights_for_lead_policy(self, prev_accel_constraint=True):
     a_change_cost = A_CHANGE_COST if prev_accel_constraint else 0
     W = np.asfortranarray(np.diag([X_EGO_OBSTACLE_COST, X_EGO_COST, V_EGO_COST, A_EGO_COST, a_change_cost, J_EGO_COST]))
     for i in range(N):
+      # reduce the cost on (a-a_prev) later in the horizon.
       W[4,4] = a_change_cost * np.interp(T_IDXS[i], [0.0, 1.0, 2.0], [1.0, 1.0, 0.0])
       self.solver.cost_set(i, 'W', W)
     # Setting the slice without the copy make the array not contiguous,
@@ -261,14 +262,12 @@ def set_weights_for_xva_policy(self):
       self.solver.cost_set(i, 'Zl', Zl)
 
   def set_cur_state(self, v, a):
-    if abs(self.x0[1] - v) > 2.:
-      self.x0[1] = v
-      self.x0[2] = a
+    v_prev = self.x0[1]
+    self.x0[1] = v
+    self.x0[2] = a
+    if abs(v_prev - v) > 2.: # probably only helps if v < v_prev
       for i in range(0, N+1):
         self.solver.set(i, 'x', self.x0)
-    else:
-      self.x0[1] = v
-      self.x0[2] = a
 
   @staticmethod
   def extrapolate_lead(x_lead, v_lead, a_lead, a_lead_tau):