chore(configs)!: drop vlm attr-path fields and the unread orchestrator.model.vlm

configs/ci/nightly/multimodal_color_codeword.toml

@@ -10,8 +10,6 @@ num_infer_gpus = 4
 name = "Qwen/Qwen3.5-4B"
 
 [model.vlm]
-vision_encoder_attr = "model.visual"
-language_model_attr = "model.language_model"
 
 [orchestrator]
 batch_size = 256

docs/advanced.md

@@ -142,10 +142,12 @@ The experimental `shard_fused_on_dim1 = true` shards fused 2-D weights along dim
 
 The built-in VLM registry covers:
 
-| Family | `model_type` | Vision attr | LM attr |
-|---|---|---|---|
-| Qwen3.5 | `qwen3_5` | `model.visual` | `model.language_model` |
-| Qwen3.5-MoE | `qwen3_5_moe` | `model.visual` | `model.language_model` |
+| Family | `model_type` |
+|---|---|
+| Qwen3.5 | `qwen3_5` |
+| Qwen3.5-MoE | `qwen3_5_moe` |
+
+The trainer finds the vision encoder at `model.visual` and the language model at `model.language_model`.
 
 ### Enabling VLM Mode
 
@@ -158,13 +160,9 @@ optimization_dtype = "bfloat16"
 reduce_dtype = "bfloat16"
 
 [model.vlm]
-vision_encoder_attr = "model.visual"
-language_model_attr = "model.language_model"
 # freeze_vision_encoder = true  # default; set false to fine-tune the encoder
 ```
 
-The weight-broadcast key prefix is derived as `{language_model_attr}.layers.` automatically.
-
 VLM training requires a registered custom PrimeRL implementation.
 
 ### Limitations

examples/extra/vlm/sft-moe.toml

@@ -13,8 +13,6 @@ ep = 8                      # keep in sync with deployment.num_train_gpus
 name = "auto"
 
 [model.vlm]
-vision_encoder_attr = "model.visual"
-language_model_attr = "model.language_model"
 freeze_vision_encoder = true
 
 [model.lora]

examples/extra/vlm/sft.toml

@@ -11,8 +11,6 @@ reduce_dtype = "bfloat16"
 name = "auto"
 
 [model.vlm]
-vision_encoder_attr = "model.visual"
-language_model_attr = "model.language_model"
 freeze_vision_encoder = true
 
 [model.lora]

packages/prime-rl-configs/src/prime_rl/configs/shared.py

@@ -183,12 +183,6 @@ def resolve_project_dir(self):
 
 
 class VLMConfig(BaseConfig):
-    vision_encoder_attr: str
-    """Dotted attribute path to the vision encoder module (e.g. ``model.visual``)."""
-
-    language_model_attr: str
-    """Dotted attribute path to the language model module (e.g. ``model.language_model``)."""
-
     freeze_vision_encoder: bool = True
     """Freeze the vision encoder parameters during training."""
 
@@ -200,9 +194,6 @@ class BaseModelConfig(BaseConfig):
     trust_remote_code: bool = False
     """Trust remote code when initializing the tokenizer."""
 
-    vlm: "VLMConfig | None" = None
-    """VLM configuration. Setting this enables vision-language model support."""
-
 
 class DynamoConfig(BaseConfig):
     enabled: bool = True

packages/prime-rl-configs/src/prime_rl/configs/trainer.py

@@ -15,6 +15,7 @@
     ResumeConfig,
     TrainerLogConfig,
     TransportConfig,
+    VLMConfig,
     WeightBroadcastConfig,
     ZMQTransportConfig,
 )
@@ -267,6 +268,9 @@ class MoERuntimeConfig(BaseConfig):
 
 
 class ModelConfig(BaseModelConfig):
+    vlm: VLMConfig | None = None
+    """VLM configuration. Setting this enables vision-language model support."""
+
     conversion_dir: Path | None = None
     """Directory for the auto-converted weights (written to a `prime`/`hf` subdirectory). If not set, we write into the model snapshot directory."""
 

packages/prime-rl-configs/src/prime_rl/utils/validation.py

@@ -71,11 +71,7 @@ def propagate(shared_path: str, *targets: str) -> None:
         "inference.vllm.model",
         "orchestrator.model.name",
     )
-    propagate(
-        "model.vlm",
-        "trainer.model.vlm",
-        "orchestrator.model.vlm",
-    )
+    propagate("model.vlm", "trainer.model.vlm")
 
     # [log]
     propagate("log.level", "trainer.log.level", "orchestrator.log.level", "inference.log.level")

tests/unit/test_configs.py

@@ -590,46 +590,13 @@ def test_multi_node_auto_inference_parallelism():
     assert config.inference.vllm.data_parallel_size == 2
 
 
-def test_orchestrator_vlm_requires_renderer():
-    with pytest.raises(ValidationError, match="renderer"):
-        OrchestratorConfig.model_validate(
-            {
-                "model": {
-                    "name": "Qwen/Qwen3-VL-4B-Instruct",
-                    "vlm": {
-                        "vision_encoder_attr": "model.visual",
-                        "language_model_attr": "model.language_model",
-                    },
-                },
-                "renderer": None,
-            }
-        )
-
-    config = OrchestratorConfig.model_validate(
-        {
-            "model": {
-                "name": "Qwen/Qwen3-VL-4B-Instruct",
-                "vlm": {
-                    "vision_encoder_attr": "model.visual",
-                    "language_model_attr": "model.language_model",
-                },
-            },
-        }
-    )
-
-    assert config.renderer is not None
-
-
 def test_trainer_rejects_vlm_cp_with_ring():
     config = {
         "model": {
             "cp": 2,
             "optimization_dtype": "bfloat16",
             "reduce_dtype": "bfloat16",
-            "vlm": {
-                "vision_encoder_attr": "model.visual",
-                "language_model_attr": "model.language_model",
-            },
+            "vlm": {},
         },
     }