diff --git a/tests/test_optim.py b/tests/test_optim.py
index ce29baed..5a0a677c 100644
--- a/tests/test_optim.py
+++ b/tests/test_optim.py
@@ -463,7 +463,7 @@ def test_adafactor(optimizer):
     _test_model(optimizer, dict(lr=5e-2))
 
 
-@pytest.mark.parametrize('optimizer',  ['lamb', 'lambw'])
+@pytest.mark.parametrize('optimizer',  ['lamb'])
 def test_lamb(optimizer):
     _test_basic_cases(
         lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
diff --git a/timm/optim/adabelief.py b/timm/optim/adabelief.py
index 531efacf..fc96e935 100644
--- a/timm/optim/adabelief.py
+++ b/timm/optim/adabelief.py
@@ -18,7 +18,7 @@ class AdaBelief(Optimizer):
         amsgrad (boolean, optional): whether to use the AMSGrad variant of this
             algorithm from the paper `On the Convergence of Adam and Beyond`_
             (default: False)
-        decoupled_decay (boolean, optional): ( default: True) If set as True, then
+        decoupled_decay (boolean, optional): (default: True) If set as True, then
             the optimizer uses decoupled weight decay as in AdamW
         fixed_decay (boolean, optional): (default: False) This is used when weight_decouple
             is set as True.
@@ -194,7 +194,7 @@ class AdaBelief(Optimizer):
                         denom = exp_avg_var.sqrt().add_(group['eps'])
                         p.data.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
                     elif step_size > 0:
-                        p.data.add_( exp_avg, alpha=-step_size * group['lr'])
+                        p.data.add_(exp_avg, alpha=-step_size * group['lr'])
                 
                 if half_precision:
                     p.data = p.data.half()
diff --git a/timm/optim/lamb.py b/timm/optim/lamb.py
index fd0e19f8..9a605f75 100644
--- a/timm/optim/lamb.py
+++ b/timm/optim/lamb.py
@@ -84,13 +84,11 @@ class Lamb(Optimizer):
     """
 
     def __init__(
-            self, params, lr=1e-3, bias_correction=True, betas=(0.9, 0.999), eps=1e-6, weight_decay=0.01,
-            grad_averaging=True, max_grad_norm=1.0, decoupled_decay=False, use_nvlamb=False):
+            self, params, lr=1e-3, bias_correction=True, betas=(0.9, 0.999), eps=1e-6,
+            weight_decay=0.01, grad_averaging=True, max_grad_norm=1.0, use_nvlamb=False):
         defaults = dict(
-            lr=lr, bias_correction=bias_correction,
-            betas=betas, eps=eps, weight_decay=weight_decay,
-            grad_averaging=grad_averaging, max_grad_norm=max_grad_norm,
-            decoupled_decay=decoupled_decay, use_nvlamb=use_nvlamb)
+            lr=lr, bias_correction=bias_correction, betas=betas, eps=eps, weight_decay=weight_decay,
+            grad_averaging=grad_averaging, max_grad_norm=max_grad_norm, use_nvlamb=use_nvlamb)
         super().__init__(params, defaults)
 
     def step(self, closure=None):
@@ -136,8 +134,6 @@ class Lamb(Optimizer):
             else:
                 group['step'] = 1
 
-            step_size = group['lr']
-
             if bias_correction:
                 bias_correction1 = 1 - beta1 ** group['step']
                 bias_correction2 = 1 - beta2 ** group['step']
@@ -157,11 +153,6 @@ class Lamb(Optimizer):
                     # Exponential moving average of squared gradient values
                     state['exp_avg_sq'] = torch.zeros_like(p.data)
 
-                decoupled_decay = group['decoupled_decay']
-                weight_decay = group['weight_decay']
-                if decoupled_decay and weight_decay != 0:
-                    p.data.mul_(1. - group['lr'] * weight_decay)
-
                 exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
 
                 # Decay the first and second moment running average coefficient
@@ -171,7 +162,8 @@ class Lamb(Optimizer):
                 denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
                 update = (exp_avg / bias_correction1).div_(denom)
 
-                if not decoupled_decay and weight_decay != 0:
+                weight_decay = group['weight_decay']
+                if weight_decay != 0:
                     update.add_(p.data, alpha=weight_decay)
 
                 trust_ratio = one_tensor
@@ -186,6 +178,6 @@ class Lamb(Optimizer):
                         one_tensor,
                     )
                 update.mul_(trust_ratio)
-                p.data.add_(update, alpha=-step_size)
+                p.data.add_(update, alpha=-group['lr'])
 
         return loss
diff --git a/timm/optim/optim_factory.py b/timm/optim/optim_factory.py
index dd7f02b8..c4a3c101 100644
--- a/timm/optim/optim_factory.py
+++ b/timm/optim/optim_factory.py
@@ -163,8 +163,6 @@ def create_optimizer_v2(
         optimizer = Adafactor(parameters, **opt_args)
     elif opt_lower == 'lamb':
         optimizer = Lamb(parameters, **opt_args)
-    elif opt_lower == 'lambw':
-        optimizer = Lamb(parameters, decoupled_decay=True, **opt_args)  # FIXME experimental
     elif opt_lower == 'madgrad':
         optimizer = MADGRAD(parameters, momentum=momentum, **opt_args)
     elif opt_lower == 'novograd' or opt_lower == 'nvnovograd':