diff --git a/src/pydvl/utils/__init__.py b/src/pydvl/utils/__init__.py
index 7beac241f..245c596dd 100644
--- a/src/pydvl/utils/__init__.py
+++ b/src/pydvl/utils/__init__.py
@@ -3,6 +3,7 @@
 from .config import *
 from .dataset import *
 from .numeric import *
+from .progress import *
 from .score import *
 from .status import *
 from .types import *
diff --git a/src/pydvl/utils/progress.py b/src/pydvl/utils/progress.py
new file mode 100644
index 000000000..bbcfca682
--- /dev/null
+++ b/src/pydvl/utils/progress.py
@@ -0,0 +1,30 @@
+from collections.abc import Iterator
+from itertools import cycle, takewhile
+from typing import Collection
+
+from tqdm.auto import tqdm
+
+from pydvl.value.result import ValuationResult
+from pydvl.value.stopping import StoppingCriterion
+
+__all__ = ["repeat_indices"]
+
+
+def repeat_indices(
+    indices: Collection[int], result: ValuationResult, done: StoppingCriterion, **kwargs
+) -> Iterator[int]:
+    """Helper function to cycle indefinitely over a collection of indices
+    until the stopping criterion is satisfied while displaying progress.
+
+    Args:
+        indices: Collection of indices that will be cycled until done.
+        result: Object containing the current results.
+        done: Stopping criterion.
+        kwargs: Keyword arguments passed to tqdm.
+    """
+    with tqdm(total=100, unit="%", **kwargs) as pbar:
+        it = takewhile(lambda _: not done(result), cycle(indices))
+        for i in it:
+            yield i
+            pbar.update(100 * done.completion() - pbar.n)
+            pbar.refresh()
diff --git a/src/pydvl/value/shapley/montecarlo.py b/src/pydvl/value/shapley/montecarlo.py
index e6f1dbf2a..aabc2d813 100644
--- a/src/pydvl/value/shapley/montecarlo.py
+++ b/src/pydvl/value/shapley/montecarlo.py
@@ -47,7 +47,6 @@
 import operator
 from concurrent.futures import FIRST_COMPLETED, Future, wait
 from functools import reduce
-from itertools import cycle, takewhile
 from typing import Optional, Sequence, Union
 
 import numpy as np
@@ -65,6 +64,7 @@
     init_parallel_backend,
 )
 from pydvl.utils.numeric import random_powerset
+from pydvl.utils.progress import repeat_indices
 from pydvl.utils.types import Seed, ensure_seed_sequence
 from pydvl.utils.utility import Utility
 from pydvl.value.result import ValuationResult
@@ -281,11 +281,10 @@ def _combinatorial_montecarlo_shapley(
     )
 
     rng = np.random.default_rng(seed)
-    repeat_indices = takewhile(lambda _: not done(result), cycle(indices))
-    pbar = tqdm(disable=not progress, position=job_id, total=100, unit="%")
-    for idx in repeat_indices:
-        pbar.n = 100 * done.completion()
-        pbar.refresh()
+
+    for idx in repeat_indices(
+        indices, result=result, done=done, disable=not progress, position=job_id
+    ):
         # Randomly sample subsets of full dataset without idx
         subset = np.setxor1d(u.data.indices, [idx], assume_unique=True)
         s = next(random_powerset(subset, n_samples=1, seed=rng))
diff --git a/src/pydvl/value/shapley/owen.py b/src/pydvl/value/shapley/owen.py
index 07b9e972b..2d7cde6ba 100644
--- a/src/pydvl/value/shapley/owen.py
+++ b/src/pydvl/value/shapley/owen.py
@@ -9,15 +9,14 @@
 import operator
 from enum import Enum
 from functools import reduce
-from itertools import cycle, takewhile
 from typing import Optional, Sequence
 
 import numpy as np
 from numpy.typing import NDArray
-from tqdm import tqdm
 
 from pydvl.parallel import MapReduceJob, ParallelConfig
 from pydvl.utils import Utility, random_powerset
+from pydvl.utils.progress import repeat_indices
 from pydvl.utils.types import Seed
 from pydvl.value import ValuationResult
 from pydvl.value.stopping import MinUpdates
@@ -76,11 +75,10 @@ def _owen_sampling_shapley(
 
     rng = np.random.default_rng(seed)
     done = MinUpdates(1)
-    repeat_indices = takewhile(lambda _: not done(result), cycle(indices))
-    pbar = tqdm(disable=not progress, position=job_id, total=100, unit="%")
-    for idx in repeat_indices:
-        pbar.n = 100 * done.completion()
-        pbar.refresh()
+
+    for idx in repeat_indices(
+        indices, result=result, done=done, disable=not progress, position=job_id
+    ):
         e = np.zeros(max_q)
         subset = np.setxor1d(u.data.indices, [idx], assume_unique=True)
         for j, q in enumerate(q_steps):