fix(rewards): harden distributional value target projection

- Clamp HL-Gauss targets
- Handle terminal mask shapes safely
- Align the default smoothing ratio with Stop Regressing

tests/rewards/test_distributional_value_function.py

@@ -120,6 +120,7 @@ def test_config_defaults_match_pi06_gemma3_layout():
     assert config.image_resolution == (448, 448)
     assert config.num_image_tokens == 256
     assert config.target_method == "dirac_delta"
+    assert config.hl_gauss_sigma_ratio == 0.75
 
 
 # ------------------------------------------------------------------
@@ -170,6 +171,15 @@ def test_hl_gauss_handles_2d_input():
     torch.testing.assert_close(dist.sum(dim=-1), torch.ones(2), atol=1e-5, rtol=0)
 
 
+@skip_if_package_missing("transformers")
+def test_hl_gauss_clamps_values_outside_support():
+    model = _make_model()
+    outside = model.hl_gauss_target(torch.tensor([-1.5, 0.5]))
+    boundaries = model.hl_gauss_target(torch.tensor([-1.0, 0.0]))
+
+    torch.testing.assert_close(outside, boundaries)
+
+
 @skip_if_package_missing("transformers")
 def test_dirac_delta_sums_to_one():
     """Dirac delta target distribution sums to 1 for each sample."""
@@ -258,6 +268,34 @@ def test_terminal_gets_one_hot():
     assert (dist[2] > 0).sum() > 2
 
 
+@skip_if_package_missing("transformers")
+def test_terminal_column_vector_preserves_batch_shape():
+    model = _make_model()
+    targets = torch.tensor([[-0.5], [-0.3]])
+    is_terminal = torch.tensor([[False], [True]])
+
+    dist = model.compute_target_distribution(
+        targets, is_terminal, method="hl_gauss", use_one_hot_terminal=True
+    )
+
+    assert dist.shape == (2, NUM_BINS)
+    assert (dist[0] > 0).sum() > 2
+    assert (dist[1] > 0).sum() == 1
+
+
+@skip_if_package_missing("transformers")
+def test_terminal_count_must_match_batch_size():
+    model = _make_model()
+
+    with pytest.raises(ValueError, match="Expected 2 terminal flags, got 1"):
+        model.compute_target_distribution(
+            torch.tensor([-0.5, -0.3]),
+            torch.tensor([True]),
+            method="hl_gauss",
+            use_one_hot_terminal=True,
+        )
+
+
 @skip_if_package_missing("transformers")
 def test_no_terminal_override_when_disabled():
     """When use_one_hot_terminal=False, terminal states use the base method."""

tests/rewards/test_temporal_siglip_value_function.py

@@ -133,3 +133,9 @@ def test_temporal_model_forward(monkeypatch):
         eval_loss, eval_metrics = model(batch)
     assert torch.isfinite(eval_loss)
     assert -1.0 <= eval_metrics["predicted_value_mean"] <= 0.0
+
+    targets = model.compute_target_distribution(
+        torch.tensor([[-0.5], [-0.3]]),
+        torch.tensor([[False], [True]]),
+    )
+    assert targets.shape == (2, model.config.num_value_bins)