diff --git a/CHANGELOG.md b/CHANGELOG.md
index c86f78bc9..382cf638e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,6 +15,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `model.rope_theta` configuration option.
 - Added `model.embedding_layer_norm` configuration option for adding a LN to the embeddings.
 - Added `model.emb_init_std` configuration option to override the standard deviation used to initialize the embeddings.
+- Added `CosLinearEnvelope` scheduler, which is a pointwise product of a cosine schedule and a linear decay.
 
 ### Changed
 
diff --git a/olmo/config.py b/olmo/config.py
index 8d3ed0823..d3f94f37c 100644
--- a/olmo/config.py
+++ b/olmo/config.py
@@ -556,6 +556,7 @@ class SchedulerType(StrEnum):
     inverse_sqrt_with_warmup = "inverse_sqrt_with_warmup"
     max_scheduler = "max_scheduler"
     constant = "constant"
+    cosine_linear_envelope = "cosine_linear_envelope"
 
 
 class SchedulerUnits(StrEnum):
diff --git a/olmo/optim.py b/olmo/optim.py
index d05536a45..5460ccee1 100644
--- a/olmo/optim.py
+++ b/olmo/optim.py
@@ -25,6 +25,7 @@
     "InvSqrtWithWarmup",
     "MaxScheduler",
     "ConstantScheduler",
+    "CosLinearEnvelope",
     "BoltOnWarmupScheduler",
     "build_optimizer",
     "build_scheduler",
@@ -788,6 +789,29 @@ def get_lr(self, initial_lr: float, step: int, max_steps: int) -> float:
         return initial_lr
 
 
+@dataclass
+class CosLinearEnvelope(Scheduler):
+    "Pointwise product of cosine schedule and linear decay; useful during annealing."
+    warmup_steps: int
+    alpha_f: float = 0.1
+    t_max: Optional[int] = None
+
+    def get_lr(self, initial_lr: float, step: int, max_steps: int) -> float:
+        max_steps = max_steps if self.t_max is None else self.t_max
+        eta_min = initial_lr * self.alpha_f
+
+        if step < self.warmup_steps:
+            return self._linear_warmup(initial_lr, step, self.warmup_steps)
+        if step >= max_steps:
+            return eta_min
+        else:
+            step = step - self.warmup_steps
+            max_steps = max_steps - self.warmup_steps
+            linear_envelope = 1 - (step / max_steps)
+            cosine_schedule = (initial_lr - eta_min) * (1 + cos(pi * step / max_steps)) / 2
+            return eta_min + linear_envelope * cosine_schedule
+
+
 PARAM_GROUP_FIELDS = ("sharded", "max_grad_norm", "max_grad_norm_ratio", "param_names")
 
 
@@ -981,5 +1005,16 @@ def build_scheduler(cfg: TrainConfig, sched_cfg: Optional[SchedulerConfig] = Non
             grad_clip_warmup_factor=sched_cfg.grad_clip_warmup_factor,
             warmup_min_lr=sched_cfg.warmup_min_lr,
         )
+    elif sched_cfg.name == SchedulerType.cosine_linear_envelope:
+        return CosLinearEnvelope(
+            grad_clip_warmup_steps=(
+                None if sched_cfg.grad_clip_warmup_steps is None else int(sched_cfg.grad_clip_warmup_steps)
+            ),
+            grad_clip_warmup_factor=sched_cfg.grad_clip_warmup_factor,
+            warmup_steps=int(sched_cfg.t_warmup),
+            alpha_f=sched_cfg.alpha_f,
+            t_max=None if sched_cfg.t_max is None else int(sched_cfg.t_max),
+            warmup_min_lr=sched_cfg.warmup_min_lr,
+        )
     else:
         raise NotImplementedError