Initial K3 snapshot: 0.5B KDA/MLA/MoE train path

Standalone tree split from LLMRL/projects/kda. Includes Triton dt_bias
backward fix, train_k3 --preset 0.5b, SFT, Docker runtime, and tests.

tests/inference/test_recurrent_decode.py

@@ -0,0 +1,30 @@
+"""L6: fused_recurrent decode matches naive recurrent."""
+import pytest
+import torch
+
+from kda.ops.recurrent.fused import fused_recurrent_kda
+from kda.ops.reference.recurrent import naive_kda
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
+
+
+def test_step_matches_naive():
+    B, T, H, HV, K, V = 2, 32, 2, 4, 8, 8
+    torch.manual_seed(20)
+    device = "cuda"
+    q = torch.randn(B, T, H, K, device=device)
+    k = torch.randn(B, T, H, K, device=device)
+    v = torch.randn(B, T, HV, V, device=device)
+    g = -torch.rand(B, T, HV, K, device=device) * 2
+    beta = torch.rand(B, T, HV, device=device)
+
+    o_naive, _ = naive_kda(
+        q.double(), k.double(), v.double(), g.double(),
+        beta.double(), output_final_state=False,
+    )
+    o_step, _ = fused_recurrent_kda(q, k, v, g, beta, output_final_state=False)
+    torch.testing.assert_close(o_step.float(), o_naive.float(), rtol=2e-3, atol=2e-3)
+
+
+if __name__ == "__main__":
+    test_step_matches_naive()