diff --git a/test/test_exploration.py b/test/test_exploration.py
index 86e883cef88..7330957f7f6 100644
--- a/test/test_exploration.py
+++ b/test/test_exploration.py
@@ -5,6 +5,7 @@
 from __future__ import annotations
 
 import argparse
+import math
 import os
 
 import pytest
@@ -891,3 +892,360 @@ def test_consistent_dropout_primer(self):
 if __name__ == "__main__":
     args, unknown = argparse.ArgumentParser().parse_known_args()
     pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown)
+
+
+@pytest.mark.parametrize("device", get_default_devices())
+class TestNoisyLinear:
+    """Tests for NoisyLinear layer based on NoisyNet paper specifications."""
+
+    def test_noisy_linear_initialization(self, device):
+        """Test that NoisyLinear initializes with correct parameters."""
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        in_features, out_features = 10, 5
+        layer = NoisyLinear(in_features, out_features, device=device)
+
+        # Check that mu and sigma parameters exist
+        assert hasattr(layer, "weight_mu")
+        assert hasattr(layer, "weight_sigma")
+        assert hasattr(layer, "bias_mu")
+        assert hasattr(layer, "bias_sigma")
+
+        # Check parameter shapes
+        assert layer.weight_mu.shape == (out_features, in_features)
+        assert layer.weight_sigma.shape == (out_features, in_features)
+        assert layer.bias_mu.shape == (out_features,)
+        assert layer.bias_sigma.shape == (out_features,)
+
+        # Check that sigma values are positive
+        assert (layer.weight_sigma > 0).all()
+        assert (layer.bias_sigma > 0).all()
+
+        # Check initialization ranges (from paper)
+        mu_range = 1 / math.sqrt(in_features)
+        assert (layer.weight_mu >= -mu_range).all()
+        assert (layer.weight_mu <= mu_range).all()
+        assert (layer.bias_mu >= -mu_range).all()
+        assert (layer.bias_mu <= mu_range).all()
+
+    def test_noisy_linear_training_vs_eval(self, device):
+        """Test that NoisyLinear behaves differently in training vs eval mode."""
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        torch.manual_seed(0)
+        layer = NoisyLinear(10, 5, device=device)
+        x = torch.randn(3, 10, device=device)
+
+        # Get outputs in training mode
+        layer.train()
+        y_train_1 = layer(x)
+        layer.reset_noise()  # Reset noise
+        y_train_2 = layer(x)
+
+        # Get outputs in eval mode
+        layer.eval()
+        y_eval_1 = layer(x)
+        layer.reset_noise()  # Reset noise
+        y_eval_2 = layer(x)
+
+        # Training outputs should be different due to noise
+        assert not torch.allclose(y_train_1, y_train_2, atol=1e-6)
+
+        # Eval outputs should be identical (no noise)
+        torch.testing.assert_close(y_eval_1, y_eval_2)
+
+        # Training and eval outputs should be different
+        assert not torch.allclose(y_train_1, y_eval_1, atol=1e-6)
+
+    def test_noise_consistency_within_episode(self, device):
+        """Test that noise remains consistent within an episode (no reset)."""
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        torch.manual_seed(0)
+        layer = NoisyLinear(10, 5, device=device)
+        layer.train()
+        x = torch.randn(3, 10, device=device)
+
+        # First forward pass
+        y1 = layer(x)
+
+        # Multiple forward passes without resetting noise
+        y2 = layer(x)
+        y3 = layer(x)
+        y4 = layer(x)
+
+        # All outputs should be identical (same noise)
+        assert torch.allclose(y1, y2, atol=1e-6)
+        assert torch.allclose(y1, y3, atol=1e-6)
+        assert torch.allclose(y1, y4, atol=1e-6)
+
+    def test_noise_change_after_reset(self, device):
+        """Test that noise changes after reset_noise() is called."""
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        torch.manual_seed(0)
+        layer = NoisyLinear(10, 5, device=device)
+        layer.train()
+        x = torch.randn(3, 10, device=device)
+
+        # First episode
+        y1 = layer(x)
+
+        # Reset noise (simulating new episode)
+        layer.reset_noise()
+        y2 = layer(x)
+
+        # Reset noise again
+        layer.reset_noise()
+        y3 = layer(x)
+
+        # Outputs should be different after each reset
+        assert not torch.allclose(y1, y2, atol=1e-6)
+        assert not torch.allclose(y1, y3, atol=1e-6)
+        assert not torch.allclose(y2, y3, atol=1e-6)
+
+    def test_factorized_gaussian_noise(self, device):
+        """Test that the noise follows factorized Gaussian distribution."""
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        torch.manual_seed(0)
+        layer = NoisyLinear(10, 5, device=device)
+        layer.train()
+
+        # Get noise samples
+        noise_samples = []
+        for _ in range(1000):
+            layer.reset_noise()
+            # Extract the actual noise used
+            weight_noise = layer.weight - layer.weight_mu
+            noise_samples.append(weight_noise.flatten())
+
+        noise_samples = torch.stack(noise_samples)
+
+        # Check that noise has approximately zero mean
+        assert abs(noise_samples.mean()) < 0.1
+
+        # Check that noise has reasonable variance
+        noise_std = noise_samples.std()
+        expected_std = layer.std_init / math.sqrt(10)  # Based on initialization
+        assert 0.5 * expected_std < noise_std < 2.0 * expected_std
+
+    def test_weight_property_behavior(self, device):
+        """Test that weight property returns correct values in train/eval modes."""
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        torch.manual_seed(0)
+        layer = NoisyLinear(10, 5, device=device)
+
+        # Training mode
+        layer.train()
+        layer.reset_noise()
+        weight_train = layer.weight
+        bias_train = layer.bias
+
+        # Should include noise
+        assert not torch.allclose(weight_train, layer.weight_mu, atol=1e-6)
+        assert not torch.allclose(bias_train, layer.bias_mu, atol=1e-6)
+
+        # Eval mode
+        layer.eval()
+        weight_eval = layer.weight
+        bias_eval = layer.bias
+
+        # Should be exactly the mean weights
+        assert torch.allclose(weight_eval, layer.weight_mu, atol=1e-6)
+        assert torch.allclose(bias_eval, layer.bias_mu, atol=1e-6)
+
+    def test_noisy_linear_in_network(self, device):
+        """Test NoisyLinear in a complete network setup."""
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        torch.manual_seed(0)
+
+        # Create a simple network with NoisyLinear
+        network = nn.Sequential(
+            nn.Linear(10, 20), nn.ReLU(), NoisyLinear(20, 5, device=device)
+        ).to(device)
+
+        x = torch.randn(3, 10, device=device)
+
+        # Training mode
+        network.train()
+        y_train_1 = network(x)
+        network[-1].reset_noise()  # Reset noise in NoisyLinear layer
+        y_train_2 = network(x)
+
+        # Eval mode
+        network.eval()
+        y_eval_1 = network(x)
+        y_eval_2 = network(x)
+
+        # Training outputs should be different
+        assert not torch.allclose(y_train_1, y_train_2, atol=1e-6)
+
+        # Eval outputs should be identical
+        assert torch.allclose(y_eval_1, y_eval_2, atol=1e-6)
+
+    def test_noise_reset_function(self, device):
+        """Test the reset_noise utility function."""
+        from torchrl.modules.models.exploration import NoisyLinear, reset_noise
+
+        torch.manual_seed(0)
+
+        # Create network with multiple NoisyLinear layers
+        network = nn.Sequential(
+            NoisyLinear(10, 20, device=device),
+            nn.ReLU(),
+            NoisyLinear(20, 5, device=device),
+        ).to(device)
+
+        network.train()
+        x = torch.randn(3, 10, device=device)
+
+        # First forward pass
+        network(x)
+
+        # Reset noise using utility function
+        reset_noise(network)
+        network(x)
+
+        # Outputs should be different (but might be the same if noise is very small)
+        # Let's check that at least one of the layers changed
+        changed = False
+        for module in network.modules():
+            if hasattr(module, "weight_mu"):
+                # Check if the actual weights changed
+                if not torch.allclose(module.weight, module.weight_mu, atol=1e-6):
+                    changed = True
+                    break
+
+        # If no noise is present, the test should still pass
+        if not changed:
+            # Check that we're in eval mode or noise is very small
+            assert network.training == False or all(
+                hasattr(m, "weight_sigma") and m.weight_sigma.max() < 1e-3
+                for m in network.modules()
+                if hasattr(m, "weight_sigma")
+            )
+
+    def test_noisy_linear_gradients(self, device):
+        """Test that gradients flow through NoisyLinear parameters."""
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        torch.manual_seed(0)
+        layer = NoisyLinear(10, 5, device=device)
+        layer.train()
+
+        x = torch.randn(3, 10, device=device, requires_grad=True)
+        y = layer(x)
+        loss = y.sum()
+
+        # Backward pass
+        loss.backward()
+
+        # Check that gradients exist for all parameters
+        assert layer.weight_mu.grad is not None
+        assert layer.weight_sigma.grad is not None
+        assert layer.bias_mu.grad is not None
+        assert layer.bias_sigma.grad is not None
+
+        # Check that gradients are not zero
+        assert not torch.allclose(
+            layer.weight_mu.grad, torch.zeros_like(layer.weight_mu.grad)
+        )
+        assert not torch.allclose(
+            layer.weight_sigma.grad, torch.zeros_like(layer.weight_sigma.grad)
+        )
+
+    def test_noisy_linear_parameter_learning(self, device):
+        """Test that sigma parameters actually learn during training."""
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        torch.manual_seed(0)
+        layer = NoisyLinear(10, 5, device=device)
+        layer.train()
+
+        # Store initial sigma values
+        initial_weight_sigma = layer.weight_sigma.clone()
+        initial_bias_sigma = layer.bias_sigma.clone()
+
+        # Simple training loop
+        optimizer = torch.optim.Adam(layer.parameters(), lr=0.01)
+        x = torch.randn(100, 10, device=device)
+        target = torch.randn(100, 5, device=device)
+
+        for _ in range(10):
+            optimizer.zero_grad()
+            layer.reset_noise()  # Reset noise each iteration
+            y = layer(x)
+            loss = torch.nn.functional.mse_loss(y, target)
+            loss.backward()
+            optimizer.step()
+
+        # Check that sigma values have changed
+        assert not torch.allclose(layer.weight_sigma, initial_weight_sigma, atol=1e-6)
+        assert not torch.allclose(layer.bias_sigma, initial_bias_sigma, atol=1e-6)
+
+    def test_noisy_linear_std_init_effect(self, device):
+        """Test that different std_init values affect noise magnitude."""
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        torch.manual_seed(0)
+
+        # Create layers with different std_init values
+        layer_small = NoisyLinear(10, 5, std_init=0.01, device=device)
+        layer_large = NoisyLinear(10, 5, std_init=1.0, device=device)
+
+        layer_small.train()
+        layer_large.train()
+
+        x = torch.randn(3, 10, device=device)
+
+        # Get outputs with different noise levels
+        layer_small.reset_noise()
+        layer_large.reset_noise()
+
+        # Get multiple samples to measure noise variance
+        noise_samples_small = []
+        noise_samples_large = []
+
+        for _ in range(10):
+            layer_small.reset_noise()
+            layer_large.reset_noise()
+            y_small = layer_small(x)
+            y_large = layer_large(x)
+            noise_samples_small.append(y_small)
+            noise_samples_large.append(y_large)
+
+        noise_samples_small = torch.stack(noise_samples_small)
+        noise_samples_large = torch.stack(noise_samples_large)
+
+        # Calculate noise variance
+        noise_var_small = noise_samples_small.var(dim=0).mean()
+        noise_var_large = noise_samples_large.var(dim=0).mean()
+
+        # Large std_init should produce larger noise variance
+        assert noise_var_large > noise_var_small
+
+    def test_noisy_linear_serialization(self, device):
+        """Test that NoisyLinear can be saved and loaded correctly."""
+        import os
+        import tempfile
+
+        from torchrl.modules.models.exploration import NoisyLinear
+
+        torch.manual_seed(0)
+        layer = NoisyLinear(10, 5, device=device)
+
+        # Save and load
+        with tempfile.NamedTemporaryFile(delete=False) as f:
+            torch.save(layer.state_dict(), f.name)
+            layer_loaded = NoisyLinear(10, 5, device=device)
+            layer_loaded.load_state_dict(torch.load(f.name))
+            os.unlink(f.name)
+
+        # Check that parameters are the same
+        assert torch.allclose(layer.weight_mu, layer_loaded.weight_mu, atol=1e-6)
+        assert torch.allclose(layer.weight_sigma, layer_loaded.weight_sigma, atol=1e-6)
+        assert torch.allclose(layer.bias_mu, layer_loaded.bias_mu, atol=1e-6)
+        assert torch.allclose(layer.bias_sigma, layer_loaded.bias_sigma, atol=1e-6)
diff --git a/torchrl/modules/models/utils.py b/torchrl/modules/models/utils.py
index 1ae6234a844..51fdc60d57c 100644
--- a/torchrl/modules/models/utils.py
+++ b/torchrl/modules/models/utils.py
@@ -160,3 +160,20 @@ def _reset_parameters_recursive(module, warn_if_no_op: bool = True) -> bool:
             "_reset_parameters_recursive was called without the parameters argument and did not find any parameters to reset"
         )
     return any_reset
+
+def primers_from_module(module: nn.Module, target_cls: T) -> list[TensorDictPrimer]:
+    """Get primers from a module.
+
+    Iterates over the module's children and returns the primers of the children that are instances of the target class.
+    These primers will write some data to be used by the models at reset time.
+    The tensors are set within the model during the policy call by <TODO>
+
+
+    Args:
+        module (nn.Module): the module to get primers from.
+
+    Returns:
+        list[TensorDictPrimer]: the primers from the module.
+    """
+    # 
+    ...
\ No newline at end of file