feat(vla): add SmolVLA conditioned agent with prefix encoder and train/eval validation

Introduce SmolVLA-style VLM prefix encoder backbone and IMF-AttnRes
conditioned agent. Add episode-level train/val split, action MSE
validation in training, and headless eval support.

tests/test_train_vla_transformer_optimizer.py

@@ -422,3 +422,45 @@ class TrainVLATransformerOptimizerTest(unittest.TestCase):
 
 if __name__ == '__main__':
     unittest.main()
+
+class TrainVLASmolVLAOptimizerTest(unittest.TestCase):
+    def test_build_training_optimizer_excludes_frozen_vlm_parameters_and_keeps_state_proj_and_head(self):
+        module = TrainVLATransformerOptimizerTest()._load_train_vla_module()
+
+        class _Head(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.proj = nn.Linear(2, 2)
+
+            def get_optim_groups(self, weight_decay):
+                return [{'params': list(self.parameters()), 'weight_decay': weight_decay}]
+
+        class _ConditionEncoder(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.vlm = nn.Linear(2, 2)
+                for param in self.vlm.parameters():
+                    param.requires_grad = False
+                self.state_proj = nn.Linear(2, 2)
+
+        class _Agent(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.noise_pred_net = _Head()
+                self.condition_encoder = _ConditionEncoder()
+
+        agent = _Agent()
+        with mock.patch.object(module, 'AdamW', RecordingAdamW):
+            optimizer = module.build_training_optimizer(agent, lr=1e-4, weight_decay=0.01)
+
+        names_by_param_id = {id(param): name for name, param in agent.named_parameters()}
+        optimizer_names = {
+            names_by_param_id[id(param)]
+            for group in optimizer.param_groups
+            for param in group['params']
+        }
+        self.assertIn('condition_encoder.state_proj.weight', optimizer_names)
+        self.assertIn('condition_encoder.state_proj.bias', optimizer_names)
+        self.assertIn('noise_pred_net.proj.weight', optimizer_names)
+        self.assertNotIn('condition_encoder.vlm.weight', optimizer_names)
+        self.assertNotIn('condition_encoder.vlm.bias', optimizer_names)