diff --git a/fastvideo/tests/modal/pr_test.py b/fastvideo/tests/modal/pr_test.py
index db30efb7d..f22a2155a 100644
--- a/fastvideo/tests/modal/pr_test.py
+++ b/fastvideo/tests/modal/pr_test.py
@@ -207,7 +207,7 @@ def run_self_forcing_tests():
 @app.function(gpu="L40S:1", image=image, timeout=900)
 def run_unit_test():
     run_test(
-        "pytest ./fastvideo/tests/api/ ./fastvideo/tests/dataset/ ./fastvideo/tests/workflow/ ./fastvideo/tests/entrypoints/ --ignore=./fastvideo/tests/entrypoints/test_openai_api_integration.py -vs"
+        "pytest ./fastvideo/tests/api/ ./fastvideo/tests/dataset/ ./fastvideo/tests/workflow/ ./fastvideo/tests/entrypoints/ ./fastvideo/tests/train/ --ignore=./fastvideo/tests/entrypoints/test_openai_api_integration.py -vs"
     )
 
 
diff --git a/fastvideo/tests/train/__init__.py b/fastvideo/tests/train/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/fastvideo/tests/train/utils/__init__.py b/fastvideo/tests/train/utils/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/fastvideo/tests/train/utils/test_config.py b/fastvideo/tests/train/utils/test_config.py
new file mode 100644
index 000000000..f23b5ced8
--- /dev/null
+++ b/fastvideo/tests/train/utils/test_config.py
@@ -0,0 +1,314 @@
+# SPDX-License-Identifier: Apache-2.0
+"""CPU-only unit tests for :func:`load_run_config`."""
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import pytest
+import yaml
+
+from fastvideo.train.utils.config import RunConfig, load_run_config
+from fastvideo.train.utils.training_config import TrainingConfig
+
+
+def _write_yaml(tmp_path: Path, data: dict[str, Any]) -> str:
+    path = tmp_path / "run.yaml"
+    path.write_text(yaml.safe_dump(data), encoding="utf-8")
+    return str(path)
+
+
+def _minimal_yaml() -> dict[str, Any]:
+    return {
+        "models": {
+            "student": {
+                "_target_": "fastvideo.train.models.wan.WanModel",
+                "init_from": "fake/model",
+            },
+        },
+        "method": {
+            "_target_":
+            "fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod",
+        },
+        "training": {},
+    }
+
+
+# ---------------------------------------------------------------------------
+# Happy path
+# ---------------------------------------------------------------------------
+
+
+def test_minimal_yaml_loads_happy_path(tmp_path: Path) -> None:
+    cfg = load_run_config(_write_yaml(tmp_path, _minimal_yaml()))
+
+    assert isinstance(cfg, RunConfig)
+    assert isinstance(cfg.training, TrainingConfig)
+    assert cfg.method["_target_"] == (
+        "fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod")
+    assert "student" in cfg.models
+    assert cfg.callbacks == {}
+    # raw retains the original YAML dict for downstream logging.
+    assert "models" in cfg.raw and "method" in cfg.raw
+
+
+def test_minimal_yaml_applies_all_defaults(tmp_path: Path) -> None:
+    cfg = load_run_config(_write_yaml(tmp_path, _minimal_yaml()))
+    t = cfg.training
+
+    assert t.distributed.num_gpus == 1
+    assert t.distributed.tp_size == 1
+    assert t.distributed.sp_size == 1
+    assert t.distributed.hsdp_replicate_dim == 1
+    assert t.distributed.pin_cpu_memory is False
+
+    assert t.data.train_batch_size == 1
+    assert t.data.dataloader_num_workers == 0
+    assert t.data.training_cfg_rate == 0.0
+    assert t.data.seed == 0
+
+    assert t.optimizer.learning_rate == 0.0
+    assert t.optimizer.betas == (0.9, 0.999)
+    assert t.optimizer.weight_decay == 0.0
+    assert t.optimizer.lr_scheduler == "constant"
+    assert t.optimizer.min_lr_ratio == 0.5
+
+    assert t.loop.max_train_steps == 0
+    assert t.loop.gradient_accumulation_steps == 1
+
+    assert t.checkpoint.output_dir == ""
+    assert t.checkpoint.checkpoints_total_limit == 0
+
+    assert t.tracker.trackers == []
+    assert t.tracker.project_name == "fastvideo"
+    assert t.tracker.run_name == ""
+
+    assert t.model.weighting_scheme == "uniform"
+    assert t.model.precondition_outputs is False
+    assert t.model.moba_config == {}
+
+    assert t.dit_precision == "fp32"
+    assert t.vsa_sparsity == 0.0
+    assert t.pipeline_config is None
+
+
+def test_full_yaml_populates_all_training_fields(tmp_path: Path) -> None:
+    data = _minimal_yaml()
+    data["training"] = {
+        "distributed": {
+            "num_gpus": 4,
+            "tp_size": 2,
+            "sp_size": 2,
+            "hsdp_replicate_dim": 2,
+            "hsdp_shard_dim": 2,
+            "pin_cpu_memory": True,
+        },
+        "data": {
+            "data_path": "/some/path",
+            "train_batch_size": 2,
+            "dataloader_num_workers": 4,
+            "training_cfg_rate": 0.1,
+            "seed": 42,
+            "num_height": 256,
+            "num_width": 512,
+            "num_latent_t": 8,
+            "num_frames": 33,
+        },
+        "optimizer": {
+            "learning_rate": 1e-4,
+            "betas": [0.9, 0.95],
+            "weight_decay": 0.01,
+            "lr_scheduler": "cosine",
+            "lr_warmup_steps": 100,
+            "min_lr_ratio": 0.1,
+        },
+        "loop": {
+            "max_train_steps": 1000,
+            "gradient_accumulation_steps": 4,
+        },
+        "checkpoint": {
+            "output_dir": "/out",
+            "training_state_checkpointing_steps": 50,
+            "checkpoints_total_limit": 3,
+        },
+        "tracker": {
+            "trackers": ["wandb"],
+            "project_name": "myproj",
+            "run_name": "myrun",
+        },
+        "vsa": {
+            "sparsity": 0.5
+        },
+        "model": {
+            "weighting_scheme": "logit_normal",
+            "logit_mean": 0.5,
+            "logit_std": 1.5,
+            "precondition_outputs": True,
+        },
+        "dit_precision": "bf16",
+    }
+    cfg = load_run_config(_write_yaml(tmp_path, data))
+    t = cfg.training
+
+    assert t.distributed.num_gpus == 4
+    assert t.distributed.tp_size == 2
+    assert t.distributed.pin_cpu_memory is True
+
+    assert t.data.train_batch_size == 2
+    assert t.data.num_frames == 33
+    assert t.data.seed == 42
+
+    assert t.optimizer.learning_rate == pytest.approx(1e-4)
+    assert t.optimizer.betas == (0.9, 0.95)
+    assert t.optimizer.lr_scheduler == "cosine"
+    assert t.optimizer.lr_warmup_steps == 100
+    assert t.optimizer.min_lr_ratio == pytest.approx(0.1)
+
+    assert t.loop.max_train_steps == 1000
+    assert t.loop.gradient_accumulation_steps == 4
+
+    assert t.checkpoint.output_dir == "/out"
+    assert t.checkpoint.checkpoints_total_limit == 3
+
+    assert t.tracker.trackers == ["wandb"]
+    assert t.tracker.project_name == "myproj"
+
+    assert t.vsa_sparsity == pytest.approx(0.5)
+    assert t.model.weighting_scheme == "logit_normal"
+    assert t.model.precondition_outputs is True
+    assert t.dit_precision == "bf16"
+
+
+# ---------------------------------------------------------------------------
+# Schema validation
+# ---------------------------------------------------------------------------
+
+
+def test_missing_models_raises(tmp_path: Path) -> None:
+    data = _minimal_yaml()
+    del data["models"]
+    with pytest.raises(ValueError, match="models"):
+        load_run_config(_write_yaml(tmp_path, data))
+
+
+def test_missing_method_raises(tmp_path: Path) -> None:
+    data = _minimal_yaml()
+    del data["method"]
+    with pytest.raises(ValueError, match="method"):
+        load_run_config(_write_yaml(tmp_path, data))
+
+
+def test_missing_training_raises(tmp_path: Path) -> None:
+    data = _minimal_yaml()
+    del data["training"]
+    with pytest.raises(ValueError, match="training"):
+        load_run_config(_write_yaml(tmp_path, data))
+
+
+def test_models_role_without_target_raises(tmp_path: Path) -> None:
+    data = _minimal_yaml()
+    data["models"]["student"] = {"init_from": "fake/model"}
+    with pytest.raises(ValueError, match="_target_"):
+        load_run_config(_write_yaml(tmp_path, data))
+
+
+def test_method_without_target_raises(tmp_path: Path) -> None:
+    data = _minimal_yaml()
+    data["method"] = {"some_param": 5}
+    with pytest.raises(ValueError, match="_target_"):
+        load_run_config(_write_yaml(tmp_path, data))
+
+
+def test_missing_config_file_raises(tmp_path: Path) -> None:
+    with pytest.raises(FileNotFoundError):
+        load_run_config(str(tmp_path / "does_not_exist.yaml"))
+
+
+# ---------------------------------------------------------------------------
+# Special parsing
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("betas_value, expected", [
+    ([0.8, 0.9], (0.8, 0.9)),
+    ("0.9,0.999", (0.9, 0.999)),
+])
+def test_betas_parses_list_and_string_forms(
+    tmp_path: Path,
+    betas_value: Any,
+    expected: tuple[float, float],
+) -> None:
+    data = _minimal_yaml()
+    data["training"] = {"optimizer": {"betas": betas_value}}
+    cfg = load_run_config(_write_yaml(tmp_path, data))
+    assert cfg.training.optimizer.betas == expected
+
+
+def test_dotted_overrides_apply_with_type_coercion(tmp_path: Path) -> None:
+    path = _write_yaml(tmp_path, _minimal_yaml())
+    overrides = [
+        "--training.distributed.num_gpus=4",
+        "--training.optimizer.learning_rate=1e-3",
+        "--training.distributed.pin_cpu_memory=true",
+        "--training.tracker.project_name=overridden",
+    ]
+    cfg = load_run_config(path, overrides=overrides)
+
+    assert cfg.training.distributed.num_gpus == 4
+    assert cfg.training.optimizer.learning_rate == pytest.approx(1e-3)
+    assert cfg.training.distributed.pin_cpu_memory is True
+    assert cfg.training.tracker.project_name == "overridden"
+
+
+def test_dotted_overrides_accept_separate_value_token(tmp_path: Path) -> None:
+    path = _write_yaml(tmp_path, _minimal_yaml())
+    cfg = load_run_config(
+        path,
+        overrides=["--training.distributed.num_gpus", "8"],
+    )
+    assert cfg.training.distributed.num_gpus == 8
+
+
+def test_overrides_create_intermediate_keys(tmp_path: Path) -> None:
+    """Overrides into a nested key absent from YAML should still apply."""
+    data = _minimal_yaml()
+    # No `training.checkpoint` block in the minimal YAML.
+    path = _write_yaml(tmp_path, data)
+    cfg = load_run_config(
+        path,
+        overrides=["--training.checkpoint.checkpoints_total_limit=5"],
+    )
+    assert cfg.training.checkpoint.checkpoints_total_limit == 5
+
+
+def test_hsdp_shard_dim_defaults_to_num_gpus(tmp_path: Path) -> None:
+    """When unset, hsdp_shard_dim and sp_size fall back to num_gpus."""
+    data = _minimal_yaml()
+    data["training"] = {"distributed": {"num_gpus": 4}}
+    cfg = load_run_config(_write_yaml(tmp_path, data))
+    assert cfg.training.distributed.hsdp_shard_dim == 4
+    assert cfg.training.distributed.sp_size == 4
+
+
+def test_model_path_derived_from_student_init_from(tmp_path: Path) -> None:
+    cfg = load_run_config(_write_yaml(tmp_path, _minimal_yaml()))
+    assert cfg.training.model_path == "fake/model"
+
+
+def test_callbacks_default_to_empty_when_absent(tmp_path: Path) -> None:
+    cfg = load_run_config(_write_yaml(tmp_path, _minimal_yaml()))
+    assert cfg.callbacks == {}
+
+
+def test_callbacks_passed_through_when_present(tmp_path: Path) -> None:
+    data = _minimal_yaml()
+    data["callbacks"] = {
+        "grad_clip": {
+            "_target_":
+            "fastvideo.train.callbacks.grad_clip.GradNormClipCallback",
+            "max_grad_norm": 1.0,
+        },
+    }
+    cfg = load_run_config(_write_yaml(tmp_path, data))
+    assert "grad_clip" in cfg.callbacks
+    assert cfg.callbacks["grad_clip"]["max_grad_norm"] == 1.0