test: prune low-value and duplicate tests

- Remove tautological test_trainer assertions that never trained
- Drop grpo isfinite-only smokes and merge frozen-model checks via parametrize
- Merge duplicate tool_parser cases (find/streaming/factory) with parametrize
- Collapse duplicate dataset store/detect_format tests
- Remove misleading scheduler/task tests that asserted the opposite of their names
- Merge signal-handler SIGTERM/SIGINT into one parametrized case
- Drop cross-file grpo strategy duplication kept in online_strategy

tests/trainer/test_trainer.py

@@ -1,13 +1,13 @@
+import pytest
+
 from astrai.trainer import Trainer
 
-# train_config_factory is injected via fixture
 
-
-def test_different_batch_sizes(base_test_env, random_dataset, train_config_factory):
-    """Test training with different batch sizes"""
-    batch_sizes = [1, 2, 4, 8]
-
-    for batch_per_device in batch_sizes:
+def test_training_runs_with_various_batch_sizes(
+    base_test_env, random_dataset, train_config_factory
+):
+    """Training should complete for a range of batch sizes without error."""
+    for batch_per_device in [1, 2, 4]:
         train_config = train_config_factory(
             model_fn=lambda: base_test_env["model"],
             dataset=random_dataset,
@@ -15,48 +15,22 @@ def test_different_batch_sizes(base_test_env, random_dataset, train_config_facto
             device=base_test_env["device"],
             batch_per_device=batch_per_device,
         )
-
-        assert train_config.batch_per_device == batch_per_device
-
-
-def test_gradient_accumulation(base_test_env, random_dataset, train_config_factory):
-    """Test training with different gradient accumulation steps"""
-    grad_accum_steps_list = [1, 2, 4]
-
-    for grad_accum_steps in grad_accum_steps_list:
-        train_config = train_config_factory(
-            model_fn=lambda: base_test_env["model"],
-            dataset=random_dataset,
-            test_dir=base_test_env["test_dir"],
-            device=base_test_env["device"],
-            batch_per_device=2,
-            grad_accum_steps=grad_accum_steps,
-        )
-
         trainer = Trainer(train_config)
         trainer.train()
 
-        assert train_config.grad_accum_steps == grad_accum_steps
 
-
-def test_memory_efficient_training(base_test_env, random_dataset, train_config_factory):
-    """Test training with memory-efficient configurations"""
-    # Test with smaller batch sizes and gradient checkpointing
-    small_batch_configs = [
-        {"batch_per_device": 1, "grad_accum_steps": 8},
-        {"batch_per_device": 2, "grad_accum_steps": 4},
-        {"batch_per_device": 4, "grad_accum_steps": 2},
-    ]
-
-    for config in small_batch_configs:
-        train_config = train_config_factory(
-            model_fn=lambda: base_test_env["model"],
-            dataset=random_dataset,
-            test_dir=base_test_env["test_dir"],
-            device=base_test_env["device"],
-            batch_per_device=config["batch_per_device"],
-            grad_accum_steps=config["grad_accum_steps"],
-        )
-
-        assert train_config.grad_accum_steps == config["grad_accum_steps"]
-        assert train_config.batch_per_device == config["batch_per_device"]
+@pytest.mark.slow
+def test_gradient_accumulation_runs(
+    base_test_env, random_dataset, train_config_factory
+):
+    """Training with gradient accumulation should complete."""
+    train_config = train_config_factory(
+        model_fn=lambda: base_test_env["model"],
+        dataset=random_dataset,
+        test_dir=base_test_env["test_dir"],
+        device=base_test_env["device"],
+        batch_per_device=2,
+        grad_accum_steps=4,
+    )
+    trainer = Trainer(train_config)
+    trainer.train()