diff --git a/src/gfn/gflownet/base.py b/src/gfn/gflownet/base.py
index 025c11bd..60d11e3b 100644
--- a/src/gfn/gflownet/base.py
+++ b/src/gfn/gflownet/base.py
@@ -71,7 +71,7 @@ def to_training_samples(self, trajectories: Trajectories) -> TrainingSampleType:
         """Converts trajectories to training samples. The type depends on the GFlowNet."""
 
     @abstractmethod
-    def loss(self, env: Env, training_objects: Any):
+    def loss(self, env: Env, training_objects: Any) -> torch.Tensor:
         """Computes the loss given the training objects."""
 
 
diff --git a/src/gfn/utils/training.py b/src/gfn/utils/training.py
index 614820b7..36afb450 100644
--- a/src/gfn/utils/training.py
+++ b/src/gfn/utils/training.py
@@ -2,9 +2,13 @@
 from typing import Dict, Optional
 
 import torch
+from tqdm import trange
 
-from gfn.env import Env
+from gfn.containers import ReplayBuffer
+from gfn.env import DiscreteEnv, Env
 from gfn.gflownet import GFlowNet, TBGFlowNet
+from gfn.gflownet.base import PFBasedGFlowNet
+from gfn.samplers import Trajectories
 from gfn.states import States
 
 
@@ -81,3 +85,111 @@ def validate(
     if logZ is not None:
         validation_info["logZ_diff"] = abs(logZ - true_logZ)
     return validation_info
+
+
+def states_actions_tns_to_traj(
+    states_tns: torch.Tensor,
+    actions_tns: torch.Tensor,
+    env: DiscreteEnv,
+    conditioning: torch.Tensor | None = None,
+) -> Trajectories:
+    """
+    This utility function helps integrate external data (e.g. expert demonstrations)
+    into the GFlowNet framework by converting raw tensors into proper Trajectories objects.
+    The downstream GFN needs to be capable of recalculating all logprobs (e.g. PFBasedGFlowNets)
+
+    Args:
+        states_tns: Tensor of shape [traj_len, *state_shape] containing states for a single trajectory
+        actions_tns: Tensor of shape [traj_len] containing discrete action indices
+        env: The discrete environment that defines the state/action spaces
+        conditioning: Tensor of shape [traj_len, *conditioning_shape] containing states for a single trajectory
+
+    Returns:
+        Trajectories: A Trajectories object containing the converted states and actions
+
+    Raises:
+        ValueError: If tensor shapes are invalid or inconsistent
+    """
+
+    if states_tns.shape[1:] != env.state_shape:
+        raise ValueError(
+            f"states_tns state dimensions must match env.state_shape {env.state_shape}, "
+            f"got shape {states_tns.shape[1:]}"
+        )
+    if len(actions_tns.shape) != 1:
+        raise ValueError(f"actions_tns must be 1D, got batch_shape {actions_tns.shape}")
+    if states_tns.shape[0] != actions_tns.shape[0] + 1:
+        raise ValueError(
+            f"states and actions must have same trajectory length, got "
+            f"states: {states_tns.shape[0]}, actions: {actions_tns.shape[0]}"
+        )
+
+    states = [env.states_from_tensor(s.unsqueeze(0)) for s in states_tns]
+    actions = [
+        env.actions_from_tensor(a.unsqueeze(0).unsqueeze(0)) for a in actions_tns
+    ]
+
+    # stack is a class method, so actions[0] is just to access a class instance and is not particularly relevant
+    actions = actions[0].stack(actions)
+    log_rewards = env.log_reward(states[-2])
+    states = states[0].stack_states(states)
+    when_is_done = torch.tensor([len(states_tns) - 1])
+
+    log_probs = None
+    estimator_outputs = None
+
+    trajectory = Trajectories(
+        env,
+        states,
+        conditioning,
+        actions,
+        log_rewards=log_rewards,
+        when_is_done=when_is_done,
+        log_probs=log_probs,
+        estimator_outputs=estimator_outputs,
+    )
+    return trajectory
+
+
+def warm_up(
+    replay_buf: ReplayBuffer,
+    optimizer: torch.optim.Optimizer,
+    gflownet: GFlowNet,
+    env: Env,
+    n_epochs: int,
+    batch_size: int,
+    recalculate_all_logprobs: bool = True,
+):
+    """
+    This utility function is an example implementation of pre-training for GFlowNets agent.
+
+    Args:
+        replay_buf: Replay Buffer, which collects Trajectories
+        optimizer: Any torch.optim optimizer (e.g. Adam, SGD)
+        gflownet: The GFlowNet to train
+        env: The environment instance
+        n_epochs: Number of epochs for warmup
+        batch_size: Number of trajectories to sample from replay buffer
+        recalculate_all_logprobs: For PFBasedGFlowNets only, force recalculating all log probs. Useful trajectories do not already have log probs.
+    Returns:
+        GFlowNet: A trained GFlowNet
+    """
+    t = trange(n_epochs, desc="Bar desc", leave=True)
+    for epoch in t:
+        training_trajs = replay_buf.sample(batch_size)
+        optimizer.zero_grad()
+        if isinstance(gflownet, PFBasedGFlowNet):
+            loss = gflownet.loss(
+                env,
+                training_trajs,
+                recalculate_all_logprobs=recalculate_all_logprobs,  # pyright: ignore
+            )  # pyright: ignore
+        else:
+            loss = gflownet.loss(env, training_trajs)
+
+        loss.backward()
+        optimizer.step()
+        t.set_description(f"{epoch=}, {loss=}")
+
+    optimizer.zero_grad()
+    return gflownet