diff --git a/src/operator/optimizer_op-inl.h b/src/operator/optimizer_op-inl.h
index 698f7977a963..146e411b447c 100644
--- a/src/operator/optimizer_op-inl.h
+++ b/src/operator/optimizer_op-inl.h
@@ -1567,7 +1567,7 @@ struct LambUpdatePhaseOneParam : public dmlc::Parameter<LambUpdatePhaseOneParam>
     float beta1;
     float beta2;
     float epsilon;
-    float t;
+    int t;
     bool bias_correction;
     float wd;
     float rescale_grad;
@@ -1623,8 +1623,8 @@ struct LambUpdatePhaseOneKernel {
   MSHADOW_XINLINE static void Map(int i, DType* out_data,
     DType* mean_data, DType* var_data, const DType* weight_data, const DType* grad_data,
     const DType clip_gradient, const DType rescale_grad,
-    const DType beta1, const DType beta2, const DType wd,
-    const DType epsilon, const DType t,
+    const DType beta1, const DType beta1_t, const DType beta2, const DType beta2_t,
+    const DType wd, const DType epsilon, const int t,
     bool bias_correction, const OpReqType req) {
     using namespace mshadow_op;
 
@@ -1639,8 +1639,8 @@ struct LambUpdatePhaseOneKernel {
     DType g = mean_data[i] / (square_root::Map(var_data[i]) + epsilon) + wd * weight_data[i];
 
     if (bias_correction) {
-      DType mean_hat = mean_data[i] / (1. - power::Map(beta1, t));
-      DType var_hat = var_data[i] / (1 - power::Map(beta2, t));
+      DType mean_hat = mean_data[i] / (1. - beta1_t);
+      DType var_hat = var_data[i] / (1 - beta2_t);
       g = mean_hat / (square_root::Map(var_hat) + epsilon) + wd * weight_data[i];
     }
     KERNEL_ASSIGN(out_data[i], req, g);
@@ -1657,6 +1657,8 @@ inline void LambUpdatePhaseOne(const nnvm::NodeAttrs& attrs,
   const LambUpdatePhaseOneParam& param = nnvm::get<LambUpdatePhaseOneParam>(attrs.parsed);
   Stream<xpu>* s = ctx.get_stream<xpu>();
   MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    DType beta1_t = std::pow(param.beta1, param.t);
+    DType beta2_t = std::pow(param.beta2, param.t);
     Tensor<xpu, 2, DType> weight = inputs[0].FlatTo2D<xpu, DType>(s);
     Tensor<xpu, 2, DType> grad = inputs[1].FlatTo2D<xpu, DType>(s);
     Tensor<xpu, 2, DType> mean = inputs[2].FlatTo2D<xpu, DType>(s);
@@ -1666,9 +1668,9 @@ inline void LambUpdatePhaseOne(const nnvm::NodeAttrs& attrs,
   Kernel<LambUpdatePhaseOneKernel, xpu>::Launch(s, weight.shape_.Size(),
     out.dptr_, mean.dptr_, var.dptr_, weight.dptr_, grad.dptr_,
     static_cast<DType>(param.clip_gradient), static_cast<DType>(param.rescale_grad),
-    static_cast<DType>(param.beta1), static_cast<DType>(param.beta2),
+    static_cast<DType>(param.beta1), beta1_t, static_cast<DType>(param.beta2), beta2_t,
     static_cast<DType>(param.wd), static_cast<DType>(param.epsilon),
-    static_cast<DType>(param.t), static_cast<bool>(param.bias_correction), req[0]);
+    static_cast<int>(param.t), static_cast<bool>(param.bias_correction), req[0]);
   });
 }