diff --git a/encodeval/eval_tasks/abstract_eval.py b/encodeval/eval_tasks/abstract_eval.py
index 1d2e05b..3081f23 100644
--- a/encodeval/eval_tasks/abstract_eval.py
+++ b/encodeval/eval_tasks/abstract_eval.py
@@ -27,6 +27,8 @@ class EvalConfig:
         task_type: Task type identifier (SC, SR, TC, or IR).
         loss_fn: Optional callable for the training loss function.
         loss_kwargs: Optional keyword arguments for the loss function.
+        finetuning_learning_rate: Learning rate used during fine-tuning, used to
+            build result directories.
     """
 
     model_class: Callable = None
@@ -40,6 +42,7 @@ class EvalConfig:
     task_type: Literal["SC", "SR", "TC", "IR"] = None
     loss_fn: Callable = None
     loss_kwargs: Dict = None
+    finetuning_learning_rate: str = None
 
     def __post_init__(self):
         """
@@ -53,10 +56,19 @@ def __post_init__(self):
         self.model_dtype = self.model_kwargs.pop("dtype")
         self.device = self.model_kwargs.pop("device")
 
+        base_output_dir = self.tr_args_kwargs.get("output_dir", "")
+        base_model_name = os.environ["EVAL_MODEL_PATH"].split("/")[-1]
+        ft_lr = None
+        if self.finetuning_learning_rate is not None:
+            ft_lr = str(self.finetuning_learning_rate).replace(".", "_").replace("-", "_").replace("+", "p")
+        model_name = f"{base_model_name}_ftlr_{ft_lr}" if ft_lr else base_model_name
+
         # Handle loading fine-tuned model from disk if specified
         ft_model_config_dir = self.model_kwargs.pop("ft_model_config_dir", None)
         if ft_model_config_dir is not None:
-            ft_model_path = f"{os.environ['EVAL_MODEL_PATH']}/evaluation/weights/{self.task_type}/{ft_model_config_dir}"
+            ft_model_path = (
+                f"{base_output_dir}/{model_name}/evaluation/weights/{self.task_type}/{ft_model_config_dir}"
+            )
             print(f"Loading fine-tuned model at {ft_model_path}")
             if "pretrained_model_name_or_path" in self.model_kwargs:
                 self.model_kwargs["pretrained_model_name_or_path"] = ft_model_path
@@ -144,15 +156,15 @@ def __post_init__(self):
             self.dataset_name = ""
 
         # Prepare output/log directories
-        model_name = os.environ["EVAL_MODEL_PATH"].split("/")[-1]
-        output_dir = self.tr_args.output_dir        
+        output_dir = base_output_dir
         output_subdir = (
             f"{self.task_type}/{self.dataset_name}/{ft_model_config_dir.replace('/', '_')}/{output_subdir}"
             if ft_model_config_dir is not None else f"{self.task_type}/{self.dataset_name}/{output_subdir}"
         )
-        self.tr_args.output_dir = f"{os.environ['EVAL_MODEL_PATH']}/evaluation/weights/{output_subdir}"
-        self.tr_args.logging_dir = f"{os.environ['EVAL_MODEL_PATH']}/evaluation/logs/{output_subdir}"
-        self.results_dir = f"{output_dir}/{model_name}/{output_subdir}"
+        model_results_root = f"{output_dir}/{model_name}"
+        self.tr_args.output_dir = f"{model_results_root}/evaluation/weights/{output_subdir}"
+        self.tr_args.logging_dir = f"{model_results_root}/evaluation/logs/{output_subdir}"
+        self.results_dir = f"{model_results_root}/{output_subdir}"
 
         # Clear old logs if logging directory is not empty
         if os.path.exists(self.tr_args.logging_dir) and len(os.listdir(self.tr_args.logging_dir)) > 0: