diff --git a/nemo/core/optim/__init__.py b/nemo/core/optim/__init__.py
index 488f4f57ea58..5abeb179dd98 100644
--- a/nemo/core/optim/__init__.py
+++ b/nemo/core/optim/__init__.py
@@ -24,6 +24,8 @@
     SquareRootAnnealing,
     T5InverseSquareRootAnnealing,
     WarmupAnnealing,
+    WarmupHoldAnnealLinear,
+    WarmupHoldAnnealOneMinusSquareRoot,
     WarmupHoldPolicy,
     WarmupPolicy,
     prepare_lr_scheduler,
diff --git a/nemo/core/optim/lr_scheduler.py b/nemo/core/optim/lr_scheduler.py
index 7697a27f6d4b..a5936fc6d41e 100644
--- a/nemo/core/optim/lr_scheduler.py
+++ b/nemo/core/optim/lr_scheduler.py
@@ -225,6 +225,49 @@ def get_lr(self):
         return self._get_lr(step)
 
 
+class WarmupHoldAnnealOneMinusSquareRoot(WarmupHoldPolicy):
+    """Learning rate scheduler with warmup, hold, and one-minus-square-root annealing phases.
+
+    This scheduler follows a three-phase pattern:
+    1. Warmup phase: LR increases linearly from 0 to base_lr
+    2. Hold phase: LR remains constant at base_lr
+    3. Annealing phase: LR decreases following a one-minus-square-root curve from base_lr to min_lr
+
+    The annealing follows the formula: LR = base_lr * (1 - sqrt((step - hold_steps)/(max_steps - hold_steps)))
+    The min_lr is enforced after the annealing phase. i.e. the learning rate will not decay below min_lr.
+
+    Reference: https://arxiv.org/html/2408.11029
+    """
+
+    def __init__(self, optimizer, *, max_steps, last_epoch=-1, min_lr=0.0, **kwargs):
+        super().__init__(optimizer=optimizer, max_steps=max_steps, **kwargs, last_epoch=last_epoch, min_lr=min_lr)
+
+    def _get_lr(self, step):
+        mult = 1 - ((step - self.hold_steps) / (self.max_steps - self.hold_steps)) ** 0.5  # from 1 to 0
+        out_lr = [max(self.min_lr, initial_lr * mult) for initial_lr in self.base_lrs]
+        return out_lr
+
+
+class WarmupHoldAnnealLinear(WarmupHoldPolicy):
+    """Learning rate scheduler with warmup, hold, and linear annealing phases.
+
+    This scheduler follows a three-phase pattern:
+    1. Warmup phase: LR increases linearly from 0 to base_lr
+    2. Hold phase: LR remains constant at base_lr
+    3. Annealing phase: LR decreases linearly from base_lr to min_lr
+
+    Reference: https://arxiv.org/pdf/2404.06395
+    """
+
+    def __init__(self, optimizer, *, max_steps, last_epoch=-1, min_lr=0.0, **kwargs):
+        super().__init__(optimizer=optimizer, max_steps=max_steps, **kwargs, last_epoch=last_epoch, min_lr=min_lr)
+
+    def _get_lr(self, step):
+        ratio = (step - self.hold_steps) / (self.max_steps - self.hold_steps)  # from 0 to 1
+        out_lr = [initial_lr - (initial_lr - self.min_lr) * ratio for initial_lr in self.base_lrs]
+        return out_lr
+
+
 class WarmupAnnealHoldPolicy(_LRScheduler):
     """Adds warmup kwargs and warmup logic to lr policy.
     All arguments should be passed as kwargs for clarity,
@@ -1002,6 +1045,8 @@ def compute_max_steps(
     'CosineAnnealing': CosineAnnealing,
     'NoamAnnealing': NoamAnnealing,
     'NoamHoldAnnealing': NoamHoldAnnealing,
+    'WarmupHoldAnnealOneMinusSquareRoot': WarmupHoldAnnealOneMinusSquareRoot,
+    'WarmupHoldAnnealLinear': WarmupHoldAnnealLinear,
     'WarmupAnnealing': WarmupAnnealing,
     'InverseSquareRootAnnealing': InverseSquareRootAnnealing,
     'T5InverseSquareRootAnnealing': T5InverseSquareRootAnnealing,
diff --git a/nemo/lightning/pytorch/optim/__init__.py b/nemo/lightning/pytorch/optim/__init__.py
index f7af606c0916..cb7ab8c1c4ee 100644
--- a/nemo/lightning/pytorch/optim/__init__.py
+++ b/nemo/lightning/pytorch/optim/__init__.py
@@ -24,6 +24,7 @@
     SquareRootAnnealingScheduler,
     T5InverseSquareRootAnnealingScheduler,
     WarmupAnnealingScheduler,
+    WarmupHoldAnnealScheduler,
     WarmupHoldPolicyScheduler,
     WarmupPolicyScheduler,
 )
@@ -47,4 +48,5 @@
     "PolynomialHoldDecayAnnealingScheduler",
     "CosineAnnealingScheduler",
     "PytorchOptimizerModule",
+    "WarmupHoldAnnealScheduler",
 ]
diff --git a/nemo/lightning/pytorch/optim/lr_scheduler.py b/nemo/lightning/pytorch/optim/lr_scheduler.py
index 5966c7e6fcdf..40fa1c3f4c90 100644
--- a/nemo/lightning/pytorch/optim/lr_scheduler.py
+++ b/nemo/lightning/pytorch/optim/lr_scheduler.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Optional
+from typing import Literal, Optional
 
 from nemo.core.optim.lr_scheduler import (
     InverseSquareRootAnnealing,
@@ -24,6 +24,8 @@
     SquareRootAnnealing,
     T5InverseSquareRootAnnealing,
     WarmupAnnealing,
+    WarmupHoldAnnealLinear,
+    WarmupHoldAnnealOneMinusSquareRoot,
     WarmupHoldPolicy,
     WarmupPolicy,
 )
@@ -484,3 +486,60 @@ def scheduler(self, model, optimizer):
             # Metric to to monitor for schedulers like `ReduceLROnPlateau`
             "monitor": self.monitor,
         }
+
+
+class WarmupHoldAnnealScheduler(LRSchedulerModule):
+    """Warmup Hold Annealing Learning Rate Scheduler.
+
+    A learning rate scheduler with warmup, hold, and linear annealing phases.
+    """
+
+    def __init__(
+        self,
+        warmup_ratio: Optional[float] = None,
+        hold_ratio: Optional[float] = None,
+        max_steps: int = 10,
+        decay_schedule: Literal["one_minus_square_root", "linear"] = "linear",
+        min_lr: float = 0.0,
+        interval: str = "step",
+        frequency: int = 1,
+        monitor: str = "val_loss",
+    ):
+        super().__init__()
+        self.warmup_ratio = warmup_ratio
+        self.hold_ratio = hold_ratio
+        self.max_steps = max_steps
+        self.decay_schedule = decay_schedule
+        self.min_lr = min_lr
+        self.interval = interval
+        self.frequency = frequency
+        self.monitor = monitor
+
+    def scheduler(self, model, optimizer):
+        if self.decay_schedule == "one_minus_square_root":
+            lr_scheduler = WarmupHoldAnnealOneMinusSquareRoot(
+                optimizer,
+                warmup_ratio=self.warmup_ratio,
+                hold_ratio=self.hold_ratio,
+                max_steps=self.max_steps,
+                min_lr=self.min_lr,
+            )
+        elif self.decay_schedule == "linear":
+            lr_scheduler = WarmupHoldAnnealLinear(
+                optimizer,
+                warmup_ratio=self.warmup_ratio,
+                hold_ratio=self.hold_ratio,
+                max_steps=self.max_steps,
+                min_lr=self.min_lr,
+            )
+        else:
+            raise ValueError(f"Unknown decay schedule: {self.decay_schedule}")
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": {
+                "scheduler": lr_scheduler,
+                "interval": self.interval,
+                "frequency": self.frequency,
+            },
+            "monitor": self.monitor,
+        }
diff --git a/tests/core/test_optimizers_schedulers.py b/tests/core/test_optimizers_schedulers.py
index 419db309a918..698993fb3ab1 100644
--- a/tests/core/test_optimizers_schedulers.py
+++ b/tests/core/test_optimizers_schedulers.py
@@ -883,6 +883,150 @@ def test_InverseSquareRootAnnealing(self):
 
         assert final_lr == self.MIN_LR
 
+
+class TestWarmupHoldAnnealSchedulers:
+    INITIAL_LR = 0.1
+    MIN_LR = 0.01
+    MAX_STEPS = 100
+
+    @pytest.mark.unit
+    def test_WarmupHoldAnnealOneMinusSquareRoot(self):
+        model = TempModel()
+        opt_cls = optim.get_optimizer('novograd')
+        opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+        # Test case 1: No warmup, no hold
+        policy = optim.lr_scheduler.WarmupHoldAnnealOneMinusSquareRoot(
+            opt, warmup_ratio=None, hold_ratio=None, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+        )
+        initial_lr = policy.get_last_lr()[0]
+        assert initial_lr == self.INITIAL_LR
+
+        # Simulate training steps
+        lrs = []
+        for i in range(self.MAX_STEPS):
+            current_lr = policy.get_last_lr()[0]
+            lrs.append(current_lr)
+            assert current_lr <= self.INITIAL_LR
+            opt.step()
+            policy.step()
+
+        # Check final LR
+        policy.step()
+        final_lr = policy.get_last_lr()[0]
+        assert final_lr == self.MIN_LR
+
+        # Test case 2: With warmup and hold
+        warmup_ratio = 0.1  # 10% warmup
+        hold_ratio = 0.2  # 20% hold
+        warmup_steps = int(warmup_ratio * self.MAX_STEPS)
+        hold_steps = int(hold_ratio * self.MAX_STEPS)
+
+        policy = optim.lr_scheduler.WarmupHoldAnnealOneMinusSquareRoot(
+            opt, warmup_ratio=warmup_ratio, hold_ratio=hold_ratio, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+        )
+
+        initial_lr = policy.get_last_lr()[0]
+        assert initial_lr < self.INITIAL_LR  # Should start at a lower LR
+
+        # Simulate training steps
+        lrs = []
+        for i in range(self.MAX_STEPS):
+            current_lr = policy.get_last_lr()[0]
+            lrs.append(current_lr)
+
+            # During warmup, LR should increase
+            if i < warmup_steps:
+                if i > 0:
+                    assert current_lr >= lrs[i - 1]
+                assert current_lr <= self.INITIAL_LR
+
+            # During hold, LR should remain constant
+            elif i < warmup_steps + hold_steps:
+                assert abs(current_lr - self.INITIAL_LR) < 1e-6
+
+            # During annealing, LR should decrease
+            else:
+                if i > warmup_steps + hold_steps:
+                    assert current_lr <= lrs[i - 1]
+
+            opt.step()
+            policy.step()
+
+        # Check final LR
+        policy.step()
+        final_lr = policy.get_last_lr()[0]
+        assert final_lr == self.MIN_LR
+
+    @pytest.mark.unit
+    def test_WarmupHoldAnnealLinear(self):
+        model = TempModel()
+        opt_cls = optim.get_optimizer('novograd')
+        opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+        # Test case 1: No warmup, no hold
+        policy = optim.lr_scheduler.WarmupHoldAnnealLinear(
+            opt, warmup_ratio=None, hold_ratio=None, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+        )
+        initial_lr = policy.get_last_lr()[0]
+        assert initial_lr == self.INITIAL_LR
+
+        # Simulate training steps
+        lrs = []
+        for i in range(self.MAX_STEPS):
+            current_lr = policy.get_last_lr()[0]
+            lrs.append(current_lr)
+            assert current_lr <= self.INITIAL_LR
+            opt.step()
+            policy.step()
+
+        # Check final LR
+        policy.step()
+        final_lr = policy.get_last_lr()[0]
+        assert final_lr == self.MIN_LR
+
+        # Test case 2: With warmup and hold
+        warmup_ratio = 0.1  # 10% warmup
+        hold_ratio = 0.2  # 20% hold
+        warmup_steps = int(warmup_ratio * self.MAX_STEPS)
+        hold_steps = int(hold_ratio * self.MAX_STEPS)
+
+        policy = optim.lr_scheduler.WarmupHoldAnnealLinear(
+            opt, warmup_ratio=warmup_ratio, hold_ratio=hold_ratio, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+        )
+
+        initial_lr = policy.get_last_lr()[0]
+        assert initial_lr < self.INITIAL_LR  # Should start at a lower LR
+
+        # Simulate training steps
+        lrs = []
+        for i in range(self.MAX_STEPS):
+            current_lr = policy.get_last_lr()[0]
+            lrs.append(current_lr)
+
+            # During warmup, LR should increase
+            if i < warmup_steps:
+                if i > 0:
+                    assert current_lr >= lrs[i - 1]
+                assert current_lr <= self.INITIAL_LR
+
+            # During hold, LR should remain constant
+            elif i < warmup_steps + hold_steps:
+                assert abs(current_lr - self.INITIAL_LR) < 1e-6
+
+            # During annealing, LR should decrease
+            else:
+                if i > warmup_steps + hold_steps:
+                    assert current_lr <= lrs[i - 1]
+
+            opt.step()
+            policy.step()
+
+        # Check final LR
+        policy.step()
+        final_lr = policy.get_last_lr()[0]
+        assert final_lr == self.MIN_LR
+
     @pytest.mark.unit
     def test_CosineAnnealing_with_noop_steps(self):
         model = TempModel()