diff --git a/gsp_rl/src/actors/actor.py b/gsp_rl/src/actors/actor.py
index 124999c..5d1541b 100644
--- a/gsp_rl/src/actors/actor.py
+++ b/gsp_rl/src/actors/actor.py
@@ -112,6 +112,21 @@ def __init__(
             if attention:
                 self.build_gsp_network('attention')
             self.build_gsp_network('DDPG')
+
+        # Information-collapse diagnostic: last GSP learner training loss.
+        # NOTE: this is the loss returned by the GSP learner's inner learn step, which means:
+        #   - For DDPG/RDDPG/TD3 GSP schemes: actor loss (a critic-derived policy-gradient
+        #     signal), NOT the prediction MSE against delta-theta. A collapsed predictor may
+        #     not produce an anomalous value here, since the critic's value landscape can
+        #     support multiple policy solutions.
+        #   - For the attention GSP scheme: genuine prediction MSE against the label.
+        # For prediction-collapse detection, prefer the raw per-step squared error captured
+        # in RL-CollectiveTransport as `gsp_squared_error` plus the episode-level
+        # `gsp_output_std` / `gsp_pred_target_corr` attrs computed in the Stelaris HDF5Logger.
+        # Populated by learn_gsp() whenever a GSP learning step fires; reset to None at the
+        # start of each learn() call so callers can distinguish "no GSP step this tick" from
+        # "GSP step ran".
+        self.last_gsp_loss: float | None = None
             
     def build_networks(self, learning_scheme):
         if learning_scheme == 'None':
@@ -402,8 +417,11 @@ def choose_actions_batch(self, observations, networks, test=False):
                 f"Use choose_action() for RDDPG/attention networks.")
 
     def learn(self):
+        # Reset per-tick GSP loss signal. None means "no GSP step ran this tick".
+        self.last_gsp_loss = None
+
         # TODO Not sure why we have n_agents*batch_size + batch_size
-        if self.networks['replay'].mem_ctr < self.batch_size: # (self.n_agents*self.batch_size + self.batch_size): 
+        if self.networks['replay'].mem_ctr < self.batch_size: # (self.n_agents*self.batch_size + self.batch_size):
                 return
 
         if self.gsp:
@@ -431,14 +449,25 @@ def learn(self):
     def learn_gsp(self):
         if self.gsp_networks['replay'].mem_ctr < self.gsp_batch_size:
                 return
+        # Capture the inner learn call's loss so callers can observe the GSP prediction
+        # network's training loss directly (needed for the information-collapse diagnostic).
+        loss = None
         if self.gsp_networks['learning_scheme'] in {'DDPG'}:
-            self.learn_DDPG(self.gsp_networks, self.gsp, self.recurrent_gsp)
+            loss = self.learn_DDPG(self.gsp_networks, self.gsp, self.recurrent_gsp)
         elif self.gsp_networks['learning_scheme'] in {'RDDPG'}:
-            self.learn_RDDPG(self.gsp_networks, self.gsp, self.recurrent_gsp)
+            loss = self.learn_RDDPG(self.gsp_networks, self.gsp, self.recurrent_gsp)
         elif self.gsp_networks['learning_scheme'] == 'TD3':
-            self.learn_TD3(self.gsp_networks, self.gsp, self.recurrent_gsp)
+            loss = self.learn_TD3(self.gsp_networks, self.gsp, self.recurrent_gsp)
         elif self.gsp_networks['learning_scheme'] == 'attention':
-            self.learn_attention(self.gsp_networks)
+            loss = self.learn_attention(self.gsp_networks)
+        if loss is not None:
+            # TD3's non-actor-update steps return (0, 0) (critic stepped, actor did not).
+            # Recording a legitimate 0.0 there would produce false collapse signals every
+            # `update_actor_iter - 1` ticks, so skip those entries entirely — leave
+            # last_gsp_loss at None as if no GSP step ran this tick.
+            if isinstance(loss, tuple):
+                return
+            self.last_gsp_loss = float(loss)
 
     def store_agent_transition(self, s, a, r, s_, d):
         self.store_transition(s, a, r, s_, d, self.networks)
diff --git a/gsp_rl/src/actors/learning_aids.py b/gsp_rl/src/actors/learning_aids.py
index 100aeb8..4df433a 100644
--- a/gsp_rl/src/actors/learning_aids.py
+++ b/gsp_rl/src/actors/learning_aids.py
@@ -405,7 +405,7 @@ def learn_RDDPG(self, networks, gsp = False, recurrent = False):
 
         return actor_loss.item()
 
-    def learn_TD3(self, networks, gsp = False):
+    def learn_TD3(self, networks, gsp = False, recurrent = False):
         states, actions, rewards, states_, dones = self.sample_memory(networks)
 
         with T.no_grad():
diff --git a/tests/test_actor/test_gsp_loss_exposure.py b/tests/test_actor/test_gsp_loss_exposure.py
new file mode 100644
index 0000000..821ff5d
--- /dev/null
+++ b/tests/test_actor/test_gsp_loss_exposure.py
@@ -0,0 +1,130 @@
+"""Tests for exposing per-step GSP prediction loss from Actor.learn().
+
+Context: the information-collapse diagnostic (see Stelaris
+docs/specs/2026-04-12-dispatcher-diagnostic-batch.md) requires logging the
+GSP prediction network's training loss per learning step. The primary loss
+already returned from learn() is the actor/critic loss, which stays normal
+even when the GSP prediction head has collapsed to a near-constant output.
+"""
+
+import numpy as np
+import pytest
+
+from gsp_rl.src.actors.actor import Actor
+
+
+BASE_CONFIG = {
+    "GAMMA": 0.99,
+    "TAU": 0.005,
+    "ALPHA": 0.001,
+    "BETA": 0.002,
+    "LR": 0.001,
+    "EPSILON": 0.0,
+    "EPS_MIN": 0.0,
+    "EPS_DEC": 0.0,
+    "BATCH_SIZE": 8,
+    "MEM_SIZE": 100,
+    "REPLACE_TARGET_COUNTER": 10,
+    "NOISE": 0.0,
+    "UPDATE_ACTOR_ITER": 1,
+    "WARMUP": 0,
+    # Fire GSP learning every primary learn() call so the test doesn't need to iterate 100 times.
+    "GSP_LEARNING_FREQUENCY": 1,
+    "GSP_BATCH_SIZE": 8,
+}
+
+INPUT_SIZE = 8
+OUTPUT_SIZE = 4
+GSP_INPUT_SIZE = 6
+GSP_OUTPUT_SIZE = 1
+N_TRANSITIONS = 30
+
+
+def make_gsp_actor(network="DDPG"):
+    return Actor(
+        id=1,
+        config=BASE_CONFIG,
+        network=network,
+        input_size=INPUT_SIZE,
+        output_size=OUTPUT_SIZE,
+        min_max_action=1,
+        meta_param_size=1,
+        gsp=True,
+        gsp_input_size=GSP_INPUT_SIZE,
+        gsp_output_size=GSP_OUTPUT_SIZE,
+    )
+
+
+def fill_primary_and_gsp_buffers(actor, n=N_TRANSITIONS):
+    for _ in range(n):
+        # Primary network sees inputs that include the GSP head output (network_input_size)
+        s = np.random.random(actor.network_input_size).astype(np.float32)
+        s_ = np.random.random(actor.network_input_size).astype(np.float32)
+        a = actor.choose_action(s, actor.networks, test=True)
+        r = float(np.random.random())
+        actor.store_transition(s, a, r, s_, False, actor.networks)
+
+        # GSP prediction network training transitions
+        gsp_s = np.random.random(GSP_INPUT_SIZE).astype(np.float32)
+        gsp_s_ = np.random.random(GSP_INPUT_SIZE).astype(np.float32)
+        gsp_a = np.random.uniform(-1, 1, size=GSP_OUTPUT_SIZE).astype(np.float32)
+        gsp_r = float(np.random.random())
+        actor.store_gsp_transition(gsp_s, gsp_a, gsp_r, gsp_s_, False)
+
+
+class TestGSPLossExposure:
+    def test_last_gsp_loss_initialized_to_none(self):
+        actor = make_gsp_actor()
+        assert actor.last_gsp_loss is None
+
+    def test_last_gsp_loss_populated_after_learn_with_gsp(self):
+        """After primary + GSP buffers are filled and learn() runs, last_gsp_loss is a float."""
+        actor = make_gsp_actor()
+        fill_primary_and_gsp_buffers(actor)
+        actor.learn()
+        assert actor.last_gsp_loss is not None
+        assert isinstance(actor.last_gsp_loss, float)
+
+    def test_last_gsp_loss_resets_between_ticks(self):
+        """Each learn() call starts by resetting last_gsp_loss to None.
+
+        This is the load-bearing invariant of the field: consumers must be able to read
+        it after learn() and distinguish "no GSP step ran this tick" (None) from
+        "GSP step ran and returned a value" (float). If the reset fails, a stale value
+        from a previous tick bleeds into the current tick's reading.
+        """
+        actor = make_gsp_actor()
+        fill_primary_and_gsp_buffers(actor)
+        actor.learn()
+        assert actor.last_gsp_loss is not None  # populated after first learn
+
+        # Drain the GSP replay buffer below the batch size so the next learn_gsp early-returns.
+        # Simulate by swapping in an empty gsp replay buffer. This is a white-box probe of the
+        # reset invariant rather than a full end-to-end run.
+        actor.gsp_networks['replay'].mem_ctr = 0
+
+        actor.learn()
+        assert actor.last_gsp_loss is None, (
+            "last_gsp_loss should reset to None when learn() runs but no GSP step fires"
+        )
+
+    def test_last_gsp_loss_remains_none_when_gsp_disabled(self):
+        """Non-GSP actor never populates last_gsp_loss."""
+        actor = Actor(
+            id=1,
+            config=BASE_CONFIG,
+            network="DDPG",
+            input_size=INPUT_SIZE,
+            output_size=OUTPUT_SIZE,
+            min_max_action=1,
+            meta_param_size=1,
+            gsp=False,
+        )
+        for _ in range(N_TRANSITIONS):
+            s = np.random.random(INPUT_SIZE).astype(np.float32)
+            a = actor.choose_action(s, actor.networks, test=True)
+            r = float(np.random.random())
+            s_ = np.random.random(INPUT_SIZE).astype(np.float32)
+            actor.store_transition(s, a, r, s_, False, actor.networks)
+        actor.learn()
+        assert actor.last_gsp_loss is None