diff --git a/src/lightning/fabric/utilities/seed.py b/src/lightning/fabric/utilities/seed.py
index b274bce88f..da0d61845d 100644
--- a/src/lightning/fabric/utilities/seed.py
+++ b/src/lightning/fabric/utilities/seed.py
@@ -3,10 +3,11 @@ import os
 import random
 from random import getstate as python_get_rng_state
 from random import setstate as python_set_rng_state
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, List
 
 import numpy as np
 import torch
+from lightning_utilities.core.imports import RequirementCache
 
 from lightning.fabric.utilities.rank_zero import _get_rank, rank_prefixed_message, rank_zero_only, rank_zero_warn
 
@@ -75,6 +76,9 @@ def reset_seed() -> None:
     seed_everything(int(seed), workers=bool(int(workers)))
 
 
+_NUMPY_AVAILABLE = RequirementCache("numpy")
+
+
 def pl_worker_init_function(worker_id: int, rank: Optional[int] = None) -> None:  # pragma: no cover
     r"""The worker_init_fn that Lightning automatically adds to your dataloader if you previously set the seed with
     ``seed_everything(seed, workers=True)``.
@@ -91,15 +95,20 @@ def pl_worker_init_function(worker_id: int, rank: Optional[int] = None) -> None:
     log.debug(
         f"Initializing random number generators of process {global_rank} worker {worker_id} with base seed {base_seed}"
     )
-    ss = np.random.SeedSequence([base_seed, worker_id, global_rank])
-    # use 128 bits (4 x 32-bit words)
-    np.random.seed(ss.generate_state(4))
-    # Spawn distinct SeedSequences for the PyTorch PRNG and the stdlib random module
-    torch_ss, stdlib_ss = ss.spawn(2)
-    torch.manual_seed(torch_ss.generate_state(1, dtype=np.uint64)[0])
-    # use 128 bits expressed as an integer
-    stdlib_seed = (stdlib_ss.generate_state(2, dtype=np.uint64).astype(object) * [1 << 64, 1]).sum()
-    random.seed(stdlib_seed)
+    seed_sequence = _generate_seed_sequence(base_seed, worker_id, global_rank, count=4)
+    torch.manual_seed(seed_sequence[0])
+    random.seed((seed_sequence[1] << 32) | seed_sequence[2])
+    if _NUMPY_AVAILABLE:
+        np.random.seed(seed_sequence[3] & 0xFFFFFFFF)
+
+
+def _generate_seed_sequence(base_seed: int, worker_id: int, global_rank: int, count: int) -> List[int]:
+    combined_seed = (base_seed << 32) | (worker_id << 16) | global_rank
+    seeds = []
+    for _ in range(count):
+        combined_seed = (combined_seed * 6364136223846793005 + 1) & ((1 << 64) - 1)
+        seeds.append(combined_seed)
+    return seeds
 
 
 def _collect_rng_states(include_cuda: bool = True) -> Dict[str, Any]: