Remove deprecated Hosted Training val config surface

packages/prime/src/prime_cli/api/rl.py

@@ -192,7 +192,6 @@ def create_run(
         secrets: Optional[Dict[str, str]] = None,
         team_id: Optional[str] = None,
         eval_config: Optional[Dict[str, Any]] = None,
-        val_config: Optional[Dict[str, Any]] = None,
         buffer_config: Optional[Dict[str, Any]] = None,
         learning_rate: Optional[float] = None,
         lora_alpha: Optional[int] = None,
@@ -266,9 +265,6 @@ def create_run(
             if eval_config:
                 payload["eval"] = eval_config
 
-            if val_config:
-                payload["val"] = val_config
-
             if buffer_config:
                 payload["buffer"] = buffer_config
 

packages/prime/src/prime_cli/commands/rl.py

@@ -258,12 +258,6 @@ def generate_rl_config_template(environment: str | None = None) -> str:
 # num_examples = 30
 # rollouts_per_example = 4
 
-# Optional: validation during training
-# [val]
-# num_examples = 64
-# rollouts_per_example = 1
-# interval = 5
-
 # Optional: buffer configuration for difficulty filtering
 # [buffer]
 # easy_threshold = 1.0
@@ -409,24 +403,6 @@ def to_api_dict(self) -> Dict[str, Any] | None:
         return result
 
 
-class ValConfig(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    num_examples: int | None = None
-    rollouts_per_example: int | None = None
-    interval: int | None = None
-
-    def to_api_dict(self) -> Dict[str, Any] | None:
-        result: Dict[str, Any] = {}
-        if self.num_examples is not None:
-            result["num_examples"] = self.num_examples
-        if self.rollouts_per_example is not None:
-            result["rollouts_per_example"] = self.rollouts_per_example
-        if self.interval is not None:
-            result["interval"] = self.interval
-        return result if result else None
-
-
 class BufferConfig(BaseModel):
     model_config = ConfigDict(extra="forbid")
 
@@ -602,7 +578,6 @@ class RLConfig(BaseModel):
     env: List[EnvConfig] = Field(default_factory=list)
     sampling: SamplingConfig = Field(default_factory=SamplingConfig)
     eval: EvalConfig = Field(default_factory=EvalConfig)
-    val: ValConfig = Field(default_factory=ValConfig)
     buffer: BufferConfig = Field(default_factory=BufferConfig)
     wandb: WandbConfig = Field(default_factory=WandbConfig)
     checkpoints: CheckpointsConfig = Field(default_factory=CheckpointsConfig)
@@ -964,13 +939,6 @@ def _fetch_pricing() -> None:
             if cfg.eval.interval:
                 console.print(f"  Interval:     {cfg.eval.interval}")
 
-        # Validation
-        if cfg.val.num_examples is not None:
-            console.print("\n[cyan]Validation[/cyan]")
-            console.print(f"  Num Examples: {cfg.val.num_examples}")
-            if cfg.val.interval:
-                console.print(f"  Interval:     {cfg.val.interval}")
-
         # Infrastructure
         if cfg.infrastructure.compute_size:
             console.print("\n[cyan]Infrastructure[/cyan]")
@@ -1096,7 +1064,6 @@ def _format(list_p: Any, eff_p: Any) -> str:
             secrets=secrets if secrets else None,
             team_id=app_config.team_id,
             eval_config=cfg.eval.to_api_dict(),
-            val_config=cfg.val.to_api_dict(),
             buffer_config=cfg.buffer.to_api_dict(),
             learning_rate=cfg.learning_rate,
             lora_alpha=cfg.lora_alpha,

packages/prime/src/prime_lab_app/training_config.py

@@ -46,8 +46,6 @@ def training_config_toml(raw: dict[str, Any]) -> str:
 
     if isinstance(raw.get("eval_config"), dict):
         config["eval"] = _rl_eval_config(raw["eval_config"])
-    if isinstance(raw.get("val_config"), dict):
-        config["val"] = raw["val_config"]
     if isinstance(raw.get("buffer_config"), dict):
         config["buffer"] = raw["buffer_config"]
 
@@ -76,12 +74,7 @@ def normalize_rl_config(config: dict[str, Any]) -> dict[str, Any]:
             updated["eval"] = _rl_eval_config(eval_config)
     else:
         updated.pop("eval_config", None)
-    if "val_config" in updated and "val" not in updated:
-        val_config = updated.pop("val_config")
-        if isinstance(val_config, dict):
-            updated["val"] = val_config
-    else:
-        updated.pop("val_config", None)
+    updated.pop("val_config", None)
     if "buffer_config" in updated and "buffer" not in updated:
         buffer_config = updated.pop("buffer_config")
         if isinstance(buffer_config, dict):

packages/prime/tests/test_lab_view.py

@@ -1760,6 +1760,37 @@ def test_lab_view_training_config_uses_sampling_for_max_tokens() -> None:
     HostedRLConfig.model_validate(parsed)
 
 
+def test_lab_view_training_config_drops_deprecated_val_config() -> None:
+    config = _training_config_toml(
+        {
+            "base_model": "Qwen/Qwen3.5-2B",
+            "environments": ["primeintellect/alphabet-sort"],
+            "val_config": {"interval": 5},
+        }
+    )
+
+    parsed = toml.loads(config)
+    assert "val" not in parsed
+    HostedRLConfig.model_validate(parsed)
+
+
+def test_config_builder_preserves_deprecated_val_for_schema_error() -> None:
+    config = {
+        "model": "Qwen/Qwen3.5-2B",
+        "env": [{"id": "primeintellect/alphabet-sort"}],
+        "val": {"interval": 5},
+    }
+    values = {
+        "config-model": "Qwen/Qwen3.5-2B",
+        "config-envs": "primeintellect/alphabet-sort",
+    }
+
+    build = build_config_from_fields(config, "rl", lambda field_id: values.get(field_id, ""))
+
+    assert "[val]" in build.toml_text
+    assert build.errors == ("val: Extra inputs are not permitted",)
+
+
 def test_lab_config_factory_renders_shared_eval_and_rl_templates() -> None:
     eval_toml = format_lab_config(
         evaluation_config(env_id="primeintellect/wordle", num_examples=-1, max_tokens=None)

packages/prime/tests/test_rl_config.py

@@ -55,6 +55,8 @@ def test_generate_rl_config_template_keeps_default_surface_minimal() -> None:
 
     assert 'model = "Qwen/Qwen3.5-0.8B"' in template
     assert "# learning_rate = 3e-5 # optional; default is 1e-4" in template
+    assert "# [val]" not in template
+    assert "validation during training" not in template.lower()
 
     hidden_fields = [
         "oversampling_factor",
@@ -91,6 +93,18 @@ def test_flatten_config_schema_preserves_optional_array_item_types() -> None:
     assert rows["buffer.env_ratios"] == "list[number]"
 
 
+def test_load_config_rejects_deprecated_val_section(tmp_path: Path) -> None:
+    config_path = tmp_path / "rl.toml"
+    config_path.write_text(
+        'model = "dummy"\n'
+        "[val]\n"
+        "interval = 5\n"
+    )
+
+    with pytest.raises(typer.Exit):
+        load_config(str(config_path))
+
+
 def test_load_config_accepts_sampling_reasoning_effort(tmp_path: Path) -> None:
     config_path = tmp_path / "rl.toml"
     config_path.write_text('model = "openai/gpt-oss-20b"\n[sampling]\nreasoning_effort = "high"\n')

packages/prime/tests/test_train_cli.py

@@ -65,6 +65,15 @@ def test_train_init_defaults_to_rl_toml() -> None:
         assert Path("rl.toml").exists()
 
 
+def test_train_configs_omits_deprecated_val_section() -> None:
+    result = runner.invoke(app, ["train", "configs", "--output", "json"], env=TEST_ENV)
+
+    assert result.exit_code == 0, result.output
+    data = json.loads(result.stdout)
+    sections = {item["section"] for item in data["configs"]}
+    assert "val" not in sections
+
+
 def test_train_request_submits_model_request(monkeypatch) -> None:
     captured: dict[str, Any] = {}