diff --git a/examples/trl_mixin/ex_trl_distillation.py b/examples/trl_mixin/ex_trl_distillation.py
index 96cc78846..ebd14c5d2 100644
--- a/examples/trl_mixin/ex_trl_distillation.py
+++ b/examples/trl_mixin/ex_trl_distillation.py
@@ -1,7 +1,7 @@
 from sft_trainer import SFTTrainer
 from transformers import AutoModelForCausalLM, AutoTokenizer, DefaultDataCollator
 
-from llmcompressor.args import DatasetArguments, TrainingArguments
+from llmcompressor.args import DatasetArguments, ModelArguments
 from llmcompressor.transformers import TextGenerationDataset
 
 model_path = "neuralmagic/Llama-2-7b-pruned50-retrained"
@@ -16,18 +16,19 @@
 )
 
 tokenizer = AutoTokenizer.from_pretrained(model_path)
+max_seq_length = 512
 
 # Load gsm8k using SparseML dataset tools
 data_args = DatasetArguments(
-    dataset="gsm8k", dataset_config_name="main", max_seq_length=512
+    dataset="gsm8k", dataset_config_name="main", max_seq_length=max_seq_length
 )
 dataset_manager = TextGenerationDataset.load_from_registry(
     data_args.dataset,
     data_args=data_args,
     split="train",
-    tokenizer=tokenizer,
+    processor=tokenizer,
 )
-train_dataset = dataset_manager.tokenize_and_process()
+train_dataset = dataset_manager()
 print(f"--> Training Set Length = {len(train_dataset)}")
 
 # recipe for maintaining model sparsity during finetuning
@@ -48,25 +49,28 @@
 """
 
 data_collator = DefaultDataCollator()
-training_args = TrainingArguments(
+trl_sft_config_args = dict(
     output_dir=output_dir,
     num_train_epochs=0.6,
     logging_steps=50,
     gradient_checkpointing=True,
     bf16=True,
     save_safetensors=False,  # workaround for shared tensors
+    max_seq_length=max_seq_length,
+    packing=True,
 )
+model_args = ModelArguments(model=model, distill_teacher=teacher)
+
 trainer = SFTTrainer(
     model=model,
     teacher=teacher,
-    tokenizer=tokenizer,
+    processing_class=tokenizer,
     recipe=recipe,
     train_dataset=train_dataset,
     data_collator=data_collator,
-    args=training_args,
+    trl_sft_config_args=trl_sft_config_args,
     data_args=data_args,
-    max_seq_length=data_args.max_seq_length,
-    packing=True,
+    model_args=model_args,
 )
 trainer.train()
-trainer.save_model()
+trainer.save_model(output_dir)
diff --git a/src/llmcompressor/transformers/finetune/session_mixin.py b/src/llmcompressor/transformers/finetune/session_mixin.py
index e32c64f62..dc78385e9 100644
--- a/src/llmcompressor/transformers/finetune/session_mixin.py
+++ b/src/llmcompressor/transformers/finetune/session_mixin.py
@@ -79,15 +79,29 @@ def __init__(
 
         # parse training and metadata args
         training_args = kwargs.get("args")
-        self.metadata = (
-            self._extract_metadata(
+
+        self.metadata = None
+        if training_args is not None:
+            # trl_sft_trainer pathway. Both training_args and data_args
+            # have `max_seq_length` which causes collision error. This is the
+            # only shared parameter, where training arg is `TRLSFTConfig` that
+            # inherits HuggingFace's `TrainingArguments`
+            training_args_dict = training_args.to_dict()
+            if "max_seq_length" in training_args_dict:
+                training_args_dict["training_args_max_seq_length"] = (
+                    training_args_dict.pop("max_seq_length")
+                )
+                logger.warning(
+                    "Detected `max_seq_length` in both data_args ",
+                    "and training_args. This is expected for TRL in distillation. ",
+                    "Updating metadata to `training_args_max_seq_length`",
+                )
+
+            self.metadata = self._extract_metadata(
                 metadata_args=METADATA_ARGS,
-                training_args_dict=training_args.to_dict(),
+                training_args_dict=training_args_dict,
                 data_args_dict=asdict(data_args) if data_args else {},
             )
-            if training_args and METADATA_ARGS
-            else None
-        )
 
         # setup metrics and session
         self.logger_manager = LoggerManager(log_python=False)