diff --git a/bayesflow/networks/flow_matching/flow_matching.py b/bayesflow/networks/flow_matching/flow_matching.py
index da4acd321..59deab53b 100644
--- a/bayesflow/networks/flow_matching/flow_matching.py
+++ b/bayesflow/networks/flow_matching/flow_matching.py
@@ -20,12 +20,19 @@
 
 @serializable("bayesflow.networks")
 class FlowMatching(InferenceNetwork):
-    """(IN) Implements Optimal Transport Flow Matching, originally introduced as Rectified Flow, with ideas incorporated
-    from [1-3].
-
-    [1] Rectified Flow: arXiv:2209.03003
-    [2] Flow Matching: arXiv:2210.02747
-    [3] Optimal Transport Flow Matching: arXiv:2302.00482
+    """(IN) Implements Optimal Transport Flow Matching, originally introduced as Rectified Flow, with ideas
+    incorporated from [1-5].
+
+    [1] Liu et al. (2022). Flow straight and fast: Learning to generate and transfer data with rectified flow.
+        arXiv preprint arXiv:2209.03003.
+    [2] Lipman et al. (2022). Flow matching for generative modeling.
+        arXiv preprint arXiv:2210.02747.
+    [3] Tong et al. (2023). Improving and generalizing flow-based generative models with minibatch optimal transport.
+        arXiv preprint arXiv:2302.00482.
+    [4] Wildberger et al. (2023). Flow matching for scalable simulation-based inference.
+        Advances in Neural Information Processing Systems, 36, 16837-16864.
+    [5] Orsini et al. (2025). Flow matching posterior estimation for simulation-based atmospheric retrieval of
+        exoplanets. IEEE Access.
     """
 
     MLP_DEFAULT_CONFIG = {
@@ -59,6 +66,7 @@ def __init__(
         integrate_kwargs: dict[str, any] = None,
         optimal_transport_kwargs: dict[str, any] = None,
         subnet_kwargs: dict[str, any] = None,
+        time_power_law_alpha: float = 0.0,
         **kwargs,
     ):
         """
@@ -96,6 +104,9 @@ def __init__(
             into a single vector or passed as separate arguments. If set to False, the subnet
             must accept three separate inputs: 'x' (noisy parameters), 't' (time),
             and optional 'conditions'. Default is True.
+        time_power_law_alpha: float, optional
+            Changes the distribution of sampled times during training. Time is sampled from a power law distribution
+             p(t) ∝ t^(1/(1+α)), where α is the provided value. Default is α=0, which corresponds to uniform sampling.
         **kwargs
             Additional keyword arguments passed to the subnet and other components.
         """
@@ -107,6 +118,9 @@ def __init__(
         self.optimal_transport_kwargs = FlowMatching.OPTIMAL_TRANSPORT_DEFAULT_CONFIG | (optimal_transport_kwargs or {})
 
         self.loss_fn = keras.losses.get(loss_fn)
+        self.time_power_law_alpha = float(time_power_law_alpha)
+        if self.time_power_law_alpha <= -1.0:
+            raise ValueError("'time_power_law_alpha' must be greater than -1.0.")
 
         self.seed_generator = keras.random.SeedGenerator()
 
@@ -164,6 +178,7 @@ def get_config(self):
             "integrate_kwargs": self.integrate_kwargs,
             "optimal_transport_kwargs": self.optimal_transport_kwargs,
             "concatenate_subnet_input": self._concatenate_subnet_input,
+            "time_power_law_alpha": self.time_power_law_alpha,
             # we do not need to store subnet_kwargs
         }
 
@@ -307,7 +322,9 @@ def compute_metrics(
                     # conditions must be resampled along with x1
                     conditions = keras.ops.take(conditions, assignments, axis=0)
 
-            t = keras.random.uniform((keras.ops.shape(x0)[0],), seed=self.seed_generator)
+            u = keras.random.uniform((keras.ops.shape(x0)[0],), seed=self.seed_generator)
+            # p(t) ∝ t^(1/(1+α)), the inverse CDF: F^(-1)(u) = u^(1+α), α=0 is uniform
+            t = u ** (1 + self.time_power_law_alpha)
             t = expand_right_as(t, x0)
 
             x = t * x1 + (1 - t) * x0