[feat] Enhance lora_update_weight_from_tensor for RL training (#19314)

test/registered/rl/test_lora_load_from_tensor.py

@@ -329,6 +329,38 @@ class TestLoRALoadFromTensor(CustomTestCase):
 
         print("\n[Test]LoRA logprob comparison test passed!")
 
+    def test_lora_e2e_load_from_flattened_bucket(self):
+        """Test loading LoRA via FlattenedTensorBucket format (RL weight sync path)."""
+        from sglang.srt.utils import MultiprocessingSerializer
+        from sglang.srt.weight_sync.tensor_bucket import FlattenedTensorBucket
+
+        named_tensors = list(self.lora_tensors.items())
+        bucket = FlattenedTensorBucket(named_tensors=[(n, t) for n, t in named_tensors])
+        bucket_dict = {
+            "flattened_tensor": bucket.get_flattened_tensor(),
+            "metadata": bucket.get_metadata(),
+        }
+        serialized = MultiprocessingSerializer.serialize(bucket_dict, output_str=True)
+
+        result = self.engine.load_lora_adapter_from_tensors(
+            lora_name="self_cognition_Alice_flattened",
+            tensors=serialized,
+            config_dict=self.lora_config_dict,
+            load_format="flattened_bucket",
+        )
+        self.assertTrue(result.success, f"Failed: {result.error_message}")
+
+        output = self.engine.generate(
+            prompt=[TEST_PROMPT],
+            sampling_params={"max_new_tokens": MAX_NEW_TOKENS, "temperature": 0.0},
+            lora_path=["self_cognition_Alice_flattened"],
+        )
+        self.assertEqual(
+            output[0]["text"][: len(EXPECTED_OUTPUT)],
+            EXPECTED_OUTPUT,
+            "Output after applying LoRA via flattened bucket does not match expected",
+        )
+
     @classmethod
     def tearDownClass(cls):
         cls.engine.shutdown()