diff --git a/source/Rbm.cpp b/source/Rbm.cpp
index a8f566e..71d8eaa 100644
--- a/source/Rbm.cpp
+++ b/source/Rbm.cpp
@@ -367,14 +367,20 @@ void Rbm::train(arma::mat const &batch, IListener* pListener)
 			m_bh += inc_bh;
 			m_whv += inc_whv;
 
-			// Weight decay applied directly to the weights, outside the
-			// momentum recursion above -- folding it into inc_whv would let
-			// momentum geometrically amplify its effective strength to
-			// roughly weightDecay/(1-momentum), well past the configured value.
+			// Weight decay and L1 applied directly to the weights, outside the
+			// momentum recursion above -- folding either into inc_whv would let
+			// momentum geometrically amplify its effective strength to roughly
+			// lambda/(1-momentum), well past the configured value. Neither
+			// touches the biases, matching pyRBM's state_adjust().
 			if (m_params.weightDecay != 0.0)
 			{
 				m_whv -= m_params.learningRate*m_params.weightDecay*m_whv;
 			}
+			if (m_params.l1Lambda != 0.0)
+			{
+				// Subgradient of lambda*||W||_1 is lambda*sign(W) (sign(0)=0).
+				m_whv -= m_params.learningRate*m_params.l1Lambda*arma::sign(m_whv);
+			}
 
 			progress += dProgress*miniBatchSizeActual;
 			status.progress = (int)(progress + 0.5);
@@ -386,6 +392,7 @@ void Rbm::train(arma::mat const &batch, IListener* pListener)
 
 				// Calculate error
 				status.err = rms_error_accu(miniBatch - toVisibleProbs(toHiddenProbs(v_states)));
+				status.L1 = arma::accu(arma::abs(m_whv));
 				if (pListener)
 				{
 					if(!pListener->onProgress(this, status))
@@ -402,6 +409,7 @@ void Rbm::train(arma::mat const &batch, IListener* pListener)
 
 	// Update final status
 	status.err_total = rms_error_accu(batch - toVisibleProbs(toHiddenProbs(batch)));
+	status.L1 = arma::accu(arma::abs(m_whv));
 
 	if (pListener)
 	{
diff --git a/source/Rbm.hpp b/source/Rbm.hpp
index 8f93276..f90aa9e 100644
--- a/source/Rbm.hpp
+++ b/source/Rbm.hpp
@@ -28,6 +28,7 @@ public:
 		Params()
 		: learningRate(0.1)
 		, weightDecay(0.0)
+		, l1Lambda(0.0)
 		, momentum(0.9)
 		, doGaussianHidden(false)
 		, doGaussianVisible(false)
@@ -46,6 +47,7 @@ public:
 			std::cout << "Exporting Rbm::Params" << std::endl;
 			Json::Value params;
 			params["weightDecay"] = weightDecay;
+			params["l1Lambda"] = l1Lambda;
 			params["learningRate"] = learningRate;
 			params["momentum"] = momentum;
 			params["doGaussianVisible"] = (int)doGaussianVisible;
@@ -65,6 +67,7 @@ public:
 		{
 			std::cout << "Importing Rbm::Params" << std::endl;
 			weightDecay = params.get("weightDecay", weightDecay).asDouble();
+			l1Lambda = params.get("l1Lambda", l1Lambda).asDouble();
 			learningRate = params.get("learningRate", learningRate).asDouble();
 			momentum = params.get("momentum", momentum).asDouble();
 			doGaussianVisible = params.get("doGaussianVisible", doGaussianVisible) == 1;
@@ -79,6 +82,7 @@ public:
 		}
 
 		double weightDecay;
+		double l1Lambda;
 		double learningRate;
 		double momentum;
 		bool doGaussianVisible;