diff --git a/botorch/sampling/base.py b/botorch/sampling/base.py
index becfdb4814..cf31e3d7d9 100644
--- a/botorch/sampling/base.py
+++ b/botorch/sampling/base.py
@@ -10,9 +10,8 @@
 
 from __future__ import annotations
 
-import warnings
 from abc import ABC, abstractmethod
-from typing import Any, Optional, Tuple
+from typing import Optional, Tuple
 
 import torch
 from botorch.exceptions.errors import InputDataError
@@ -49,7 +48,6 @@ def __init__(
         self,
         sample_shape: torch.Size,
         seed: Optional[int] = None,
-        **kwargs: Any,
     ) -> None:
         r"""Abstract base class for samplers.
 
@@ -57,37 +55,13 @@ def __init__(
             sample_shape: The `sample_shape` of the samples to generate. The full shape
                 of the samples is given by `posterior._extended_shape(sample_shape)`.
             seed: An optional seed to use for sampling.
-            **kwargs: Catch-all for deprecated kwargs.
         """
         super().__init__()
         if not isinstance(sample_shape, torch.Size):
-            if isinstance(sample_shape, int):
-                sample_shape = torch.Size([sample_shape])
-                warnings.warn(
-                    "The first positional argument of samplers, `num_samples`, has "
-                    "been deprecated and replaced with `sample_shape`, which expects "
-                    "a `torch.Size` object.",
-                    DeprecationWarning,
-                )
-            else:
-                raise InputDataError(
-                    "Expected `sample_shape` to be a `torch.Size` object, "
-                    f"got {sample_shape}."
-                )
-        for k, v in kwargs.items():
-            if k == "resample":
-                if v is True:
-                    raise RuntimeError(KWARG_ERR_MSG.format(k, "StochasticSampler"))
-                else:
-                    warnings.warn(KWARGS_DEPRECATED_MSG.format(k), DeprecationWarning)
-            elif k == "collapse_batch_dims":
-                if v is False:
-                    raise RuntimeError(KWARG_ERR_MSG.format(k, "ForkedRNGSampler"))
-                else:
-                    warnings.warn(KWARGS_DEPRECATED_MSG.format(k), DeprecationWarning)
-            else:
-                raise RuntimeError(f"Recevied an unknown argument {k}: {v}.")
-
+            raise InputDataError(
+                "Expected `sample_shape` to be a `torch.Size` object, "
+                f"got {sample_shape}."
+            )
         self.sample_shape = sample_shape
         self.seed = seed if seed is not None else torch.randint(0, 1000000, (1,)).item()
         self.register_buffer("base_samples", None)
diff --git a/test/sampling/test_base.py b/test/sampling/test_base.py
index c4cd385043..a3373e3bd9 100644
--- a/test/sampling/test_base.py
+++ b/test/sampling/test_base.py
@@ -36,21 +36,9 @@ def test_init(self):
         # Default seed.
         sampler = NonAbstractSampler(sample_shape=torch.Size([4]))
         self.assertIsInstance(sampler.seed, int)
-        # Deprecated args & error handling.
-        with self.assertWarnsRegex(DeprecationWarning, "positional argument"):
-            NonAbstractSampler(4)
+        # Error handling.
         with self.assertRaisesRegex(InputDataError, "sample_shape"):
             NonAbstractSampler(4.5)
-        with self.assertWarnsRegex(DeprecationWarning, "resample"):
-            NonAbstractSampler(sample_shape=torch.Size([4]), resample=False)
-        with self.assertRaisesRegex(RuntimeError, "StochasticSampler"):
-            NonAbstractSampler(sample_shape=torch.Size([4]), resample=True)
-        with self.assertWarnsRegex(DeprecationWarning, "collapse_batch"):
-            NonAbstractSampler(sample_shape=torch.Size([4]), collapse_batch_dims=True)
-        with self.assertRaisesRegex(RuntimeError, "ForkedRNGSampler"):
-            NonAbstractSampler(sample_shape=torch.Size([4]), collapse_batch_dims=False)
-        with self.assertRaisesRegex(RuntimeError, "unknown argument"):
-            NonAbstractSampler(sample_shape=torch.Size([4]), dummy_arg=True)
 
     def test_batch_range(self):
         posterior = MockPosterior()
diff --git a/tutorials/Multi_objective_multi_fidelity_BO.ipynb b/tutorials/Multi_objective_multi_fidelity_BO.ipynb
index 8829c6d79d..82f55c9c7c 100644
--- a/tutorials/Multi_objective_multi_fidelity_BO.ipynb
+++ b/tutorials/Multi_objective_multi_fidelity_BO.ipynb
@@ -501,8 +501,6 @@
     "            ref_point=ref_point,\n",
     "            sampler=SobolQMCNormalSampler(\n",
     "                sample_shape=torch.Size([NUM_INNER_MC_SAMPLES]),\n",
-    "                resample=False,\n",
-    "                collapse_batch_dims=True,\n",
     "            ),\n",
     "            use_posterior_mean=True,\n",
     "        ),\n",
diff --git a/tutorials/risk_averse_bo_with_input_perturbations.ipynb b/tutorials/risk_averse_bo_with_input_perturbations.ipynb
index 89f7498182..6346adc3b2 100644
--- a/tutorials/risk_averse_bo_with_input_perturbations.ipynb
+++ b/tutorials/risk_averse_bo_with_input_perturbations.ipynb
@@ -224,7 +224,7 @@
         "    acqf = qNoisyExpectedImprovement(\n",
         "        model=model,\n",
         "        X_baseline=train_X,\n",
-        "        sampler=SobolQMCNormalSampler(128),\n",
+        "        sampler=SobolQMCNormalSampler(sample_shape=torch.Size([128])),\n",
         "        objective=risk_measure,\n",
         "        prune_baseline=True,\n",
         "    )\n",