diff --git a/test/objectives/test_values.py b/test/objectives/test_values.py
index 581ce05cfae..51f60ca8b48 100644
--- a/test/objectives/test_values.py
+++ b/test/objectives/test_values.py
@@ -38,6 +38,7 @@
 )
 from torchrl.objectives.value.functional import (
     generalized_advantage_estimate,
+    reward2go,
     td0_advantage_estimate,
     td1_advantage_estimate,
     td_lambda_advantage_estimate,
@@ -1857,6 +1858,65 @@ def test_gae(self, device, gamma, lmbda, N, T, dtype, has_done):
 
         torch.testing.assert_close(r1, r2, rtol=1e-4, atol=1e-4)
 
+    @pytest.mark.parametrize("device", get_default_devices())
+    @pytest.mark.parametrize("lmbda", [0.0, 1e-9])
+    def test_vec_estimates_zero_lmbda(self, device, lmbda):
+        # gamma * lmbda equal to zero or below the truncation threshold of the
+        # scalar fast paths must match the non-vectorized estimates
+        torch.manual_seed(0)
+        gamma = 0.9
+        done = torch.zeros(3, 8, 1, device=device, dtype=torch.bool).bernoulli_(0.2)
+        done[..., -1, :] = True
+        terminated = done & torch.zeros_like(done).bernoulli_(0.5)
+        reward = torch.randn(3, 8, 1, device=device)
+        state_value = torch.randn(3, 8, 1, device=device)
+        next_state_value = torch.randn(3, 8, 1, device=device)
+
+        r1 = vec_generalized_advantage_estimate(
+            gamma,
+            lmbda,
+            state_value,
+            next_state_value,
+            reward,
+            done=done,
+            terminated=terminated,
+        )
+        r2 = generalized_advantage_estimate(
+            gamma,
+            lmbda,
+            state_value,
+            next_state_value,
+            reward,
+            done=done,
+            terminated=terminated,
+        )
+        torch.testing.assert_close(r1, r2, rtol=1e-4, atol=1e-4)
+
+        r1 = vec_td_lambda_advantage_estimate(
+            gamma,
+            lmbda,
+            state_value,
+            next_state_value,
+            reward,
+            done=done,
+            terminated=terminated,
+        )
+        r2 = td_lambda_advantage_estimate(
+            gamma,
+            lmbda,
+            state_value,
+            next_state_value,
+            reward,
+            done=done,
+            terminated=terminated,
+        )
+        torch.testing.assert_close(r1, r2, rtol=1e-4, atol=1e-4)
+
+        # with a zero discount the reward-to-go is the reward itself
+        torch.testing.assert_close(
+            reward2go(reward, done, gamma=lmbda), reward, rtol=1e-4, atol=1e-4
+        )
+
     @pytest.mark.parametrize("device", get_default_devices())
     @pytest.mark.parametrize("N", [(1,), (8,), (7, 3)])
     @pytest.mark.parametrize("dtype", [torch.float, torch.double])
diff --git a/torchrl/objectives/value/functional.py b/torchrl/objectives/value/functional.py
index b42b3bc7148..5b6f5a691fe 100644
--- a/torchrl/objectives/value/functional.py
+++ b/torchrl/objectives/value/functional.py
@@ -194,12 +194,13 @@ def _geom_series_like(t, r, thr):
         if isinstance(r, torch.Tensor):
             r = r.item()
 
-        if r == 0.0:
-            return torch.zeros_like(t)
-        elif r >= 1.0:
+        if r >= 1.0:
             lim = t.numel()
+        elif r == 0.0:
+            lim = 1  # the series is [1, 0, 0, ...]
         else:
-            lim = int(math.log(thr) / math.log(r))
+            # r ** 0 is 1 whatever r, so the first element is always kept
+            lim = max(int(math.log(thr) / math.log(r)), 1)
 
         rs = torch.full_like(t[:lim], r)
     rs[0] = 1.0