diff --git a/build/launch_training.py b/build/launch_training.py
index 143987ad6..af02575bf 100644
--- a/build/launch_training.py
+++ b/build/launch_training.py
@@ -28,6 +28,7 @@
 # Local
 from tuning import sft_trainer
 from tuning.utils.merge_model_utils import create_merged_model
+from tuning.config.tracker_configs import TrackerConfigFactory
 from build.utils import process_launch_training_args, get_job_config
 
 
@@ -62,12 +63,23 @@ def main():
         training_args,
         tune_config,
         merge_model,
+        file_logger_config,
+        aim_config,
     ) = process_launch_training_args(job_config)
 
     original_output_dir = training_args.output_dir
     with tempfile.TemporaryDirectory() as tempdir:
         training_args.output_dir = tempdir
-        sft_trainer.train(model_args, data_args, training_args, tune_config)
+        tracker_config_args = TrackerConfigFactory(
+            file_logger_config=file_logger_config, aim_config=aim_config
+        )
+        sft_trainer.train(
+            model_args=model_args,
+            data_args=data_args,
+            train_args=training_args,
+            peft_config=tune_config,
+            tracker_configs=tracker_config_args,
+        )
 
         if merge_model:
             export_path = os.getenv(
@@ -108,7 +120,8 @@ def main():
 
         # copy over any loss logs
         train_logs_filepath = os.path.join(
-            training_args.output_dir, sft_trainer.TRAINING_LOGS_FILENAME
+            training_args.output_dir,
+            tracker_config_args.file_logger_config.training_logs_filename,
         )
         if os.path.exists(train_logs_filepath):
             shutil.copy(train_logs_filepath, original_output_dir)
diff --git a/build/utils.py b/build/utils.py
index 416661531..7025d0978 100644
--- a/build/utils.py
+++ b/build/utils.py
@@ -25,7 +25,7 @@
 from accelerate.commands.launch import launch_command_parser
 
 # Local
-from tuning.config import configs, peft_config
+from tuning.config import configs, peft_config, tracker_configs
 
 
 def txt_to_obj(txt):
@@ -67,6 +67,8 @@ def process_launch_training_args(job_config_dict):
         training_args: configs.TrainingArguments
         tune_config: peft_config.LoraConfig | peft_config.PromptTuningConfig
         merge_model: bool
+        file_logger_config: tracker_configs.FileLoggingTrackerConfig
+        aim_config: tracker_configs.AimConfig
     """
     parser = transformers.HfArgumentParser(
         dataclass_types=(
@@ -75,6 +77,8 @@ def process_launch_training_args(job_config_dict):
             configs.TrainingArguments,
             peft_config.LoraConfig,
             peft_config.PromptTuningConfig,
+            tracker_configs.FileLoggingTrackerConfig,
+            tracker_configs.AimConfig,
         )
     )
 
@@ -84,6 +88,8 @@ def process_launch_training_args(job_config_dict):
         training_args,
         lora_config,
         prompt_tuning_config,
+        file_logger_config,
+        aim_config,
     ) = parser.parse_dict(job_config_dict, allow_extra_keys=True)
 
     peft_method_parsed = job_config_dict.get("peft_method")
@@ -98,14 +104,25 @@ def process_launch_training_args(job_config_dict):
 
     logging.info(
         "Parameters used to launch training: \
-    model_args %s, data_args %s, training_args %s, tune_config %s",
+    model_args %s, data_args %s, training_args %s, tune_config %s \
+        file_logger_config %s aim_config %s",
         model_args,
         data_args,
         training_args,
         tune_config,
+        file_logger_config,
+        aim_config,
     )
 
-    return model_args, data_args, training_args, tune_config, merge_model
+    return (
+        model_args,
+        data_args,
+        training_args,
+        tune_config,
+        merge_model,
+        file_logger_config,
+        aim_config,
+    )
 
 
 def process_accelerate_launch_args(job_config_dict):
diff --git a/tests/build/test_utils.py b/tests/build/test_utils.py
index da4302153..1bfaabba4 100644
--- a/tests/build/test_utils.py
+++ b/tests/build/test_utils.py
@@ -46,6 +46,8 @@ def test_process_launch_training_args(job_config):
         training_args,
         tune_config,
         merge_model,
+        _,
+        _,
     ) = process_launch_training_args(job_config_copy)
     assert str(model_args.torch_dtype) == "torch.bfloat16"
     assert data_args.dataset_text_field == "output"
@@ -59,7 +61,7 @@ def test_process_launch_training_args_defaults(job_config):
     assert "torch_dtype" not in job_config_defaults
     assert job_config_defaults["use_flash_attn"] is False
     assert "save_strategy" not in job_config_defaults
-    model_args, _, training_args, _, _ = process_launch_training_args(
+    model_args, _, training_args, _, _, _, _ = process_launch_training_args(
         job_config_defaults
     )
     assert str(model_args.torch_dtype) == "torch.bfloat16"
@@ -70,13 +72,17 @@ def test_process_launch_training_args_defaults(job_config):
 def test_process_launch_training_args_peft_method(job_config):
     job_config_pt = copy.deepcopy(job_config)
     job_config_pt["peft_method"] = "pt"
-    _, _, _, tune_config, merge_model = process_launch_training_args(job_config_pt)
+    _, _, _, tune_config, merge_model, _, _ = process_launch_training_args(
+        job_config_pt
+    )
     assert isinstance(tune_config, PromptTuningConfig)
     assert merge_model is False
 
     job_config_lora = copy.deepcopy(job_config)
     job_config_lora["peft_method"] = "lora"
-    _, _, _, tune_config, merge_model = process_launch_training_args(job_config_lora)
+    _, _, _, tune_config, merge_model, _, _ = process_launch_training_args(
+        job_config_lora
+    )
     assert isinstance(tune_config, LoraConfig)
     assert merge_model is True