diff --git a/open_instruct/finetune.py b/open_instruct/finetune.py
index 6a998900e3..587754a654 100644
--- a/open_instruct/finetune.py
+++ b/open_instruct/finetune.py
@@ -373,6 +373,13 @@ class FlatArguments:
             "help": "Whether to clean up all previous checkpoints at the end of the run.",
         },
     )
+    final_lr_ratio: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "Set the final lr value at the end of training to be final_lr_ratio * learning_rate."
+            " Only for linear schedulers, currently."
+        },
+    )
 
     def __post_init__(self):
         if self.reduce_loss not in ["mean", "sum"]:
@@ -387,6 +394,11 @@ def __post_init__(self):
             raise ValueError("Cannot provide two dataset selection mechanisms.")
         if self.try_launch_beaker_eval_jobs and not self.push_to_hub:
             raise ValueError("Cannot launch Beaker evaluation jobs without pushing to the Hub.")
+        if self.final_lr_ratio is not None:
+            if self.lr_scheduler_type != "linear":
+                raise NotImplementedError("final_lr_ratio only currently implemented for linear schedulers")
+            if not (1.0 >= self.final_lr_ratio >= 0.0):
+                raise ValueError(f"final_lr_ratio must be between 0 and 1, not {self.final_lr_ratio=}")
 
 
 def main(args: FlatArguments, tc: TokenizerConfig):
@@ -708,11 +720,19 @@ def main(args: FlatArguments, tc: TokenizerConfig):
     num_training_steps_for_scheduler = (
         args.max_train_steps if overrode_max_train_steps else args.max_train_steps * accelerator.num_processes
     )
+
+    num_warmup_steps = int(num_training_steps_for_scheduler * args.warmup_ratio)
+    if args.final_lr_ratio is not None and args.lr_scheduler_type == "linear":
+        # Correct num_training_steps_for_scheduler to respect final_lr_ratio for a linear scheduler
+        num_training_steps_for_scheduler = (
+            num_training_steps_for_scheduler - args.final_lr_ratio * num_warmup_steps
+        ) / (1 - args.final_lr_ratio)
+
     lr_scheduler = get_scheduler(
         name=args.lr_scheduler_type,
         optimizer=optimizer,
         num_training_steps=num_training_steps_for_scheduler,
-        num_warmup_steps=int(num_training_steps_for_scheduler * args.warmup_ratio),
+        num_warmup_steps=num_warmup_steps,
     )
     # Prepare everything with `accelerator`.
     model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(