diff --git a/docs/config.qmd b/docs/config.qmd
index 800293535..7cc4a712f 100644
--- a/docs/config.qmd
+++ b/docs/config.qmd
@@ -138,7 +138,7 @@ test_datasets:
     data_files:
       - /workspace/data/eval.jsonl
 
-# use RL training: 'dpo', 'ipo', 'kto_pair', 'orpo', 'sppo'
+# use RL training: 'dpo', 'ipo', 'kto_pair', 'orpo', 'sppo_hard'
 rl:
 
 # Saves the desired chat template to the tokenizer_config.json for easier inferencing
diff --git a/requirements.txt b/requirements.txt
index 4ec2aec89..39c1623c3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -39,6 +39,6 @@ s3fs
 gcsfs
 # adlfs
 
-trl @ git+https://github.com/huggingface/trl.git@7075cec94df1a0c5be90e75214e996efaf9a6c0b
+trl @ git+https://github.com/huggingface/trl.git@75de236c09bd5846f79c24d9bf371481b0b7582c
 zstandard==0.22.0
 fastcore
diff --git a/src/axolotl/core/trainer_builder.py b/src/axolotl/core/trainer_builder.py
index 576c303b7..0974f6f61 100644
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -1526,7 +1526,7 @@ def build_training_arguments(self, total_num_steps):
         if self.cfg.rl == "orpo":
             training_args_cls = ORPOConfig
             training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-        elif self.cfg.rl in ["dpo", "ipo", "kto_pair", "sppo"]:
+        elif self.cfg.rl in ["dpo", "ipo", "kto_pair", "sppo_hard"]:
             training_args_cls = DPOConfig
             training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
 
@@ -1555,8 +1555,8 @@ def build(self, total_num_steps):
                 dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
         elif self.cfg.rl == "kto_pair":
             dpo_trainer_kwargs["loss_type"] = "kto_pair"
-        elif self.cfg.rl == "sppo":
-            dpo_trainer_kwargs["loss_type"] = "sppo"
+        elif self.cfg.rl == "sppo_hard":
+            dpo_trainer_kwargs["loss_type"] = "sppo_hard"
         if self.eval_dataset:
             dpo_trainer_kwargs["eval_dataset"] = self.eval_dataset
         if self.cfg.adapter and self.peft_config:
@@ -1565,7 +1565,7 @@ def build(self, total_num_steps):
             dpo_trainer_kwargs[
                 "precompute_ref_log_probs"
             ] = self.cfg.precompute_ref_log_probs
-        if self.cfg.rl in ["dpo", "ipo", "kto_pair", "sppo"]:
+        if self.cfg.rl in ["dpo", "ipo", "kto_pair", "sppo_hard"]:
             trainer_cls = AxolotlDPOTrainer
             dpo_trainer_kwargs["beta"] = self.cfg.dpo_beta or 0.1
             trainer_cls_args = [self.model, self.model_ref]
diff --git a/src/axolotl/utils/config/models/input/v0_4_1/__init__.py b/src/axolotl/utils/config/models/input/v0_4_1/__init__.py
index 7c00500b9..bb4476563 100644
--- a/src/axolotl/utils/config/models/input/v0_4_1/__init__.py
+++ b/src/axolotl/utils/config/models/input/v0_4_1/__init__.py
@@ -133,7 +133,7 @@ class RLType(str, Enum):
     ipo = "ipo"  # pylint: disable=invalid-name
     kto_pair = "kto_pair"  # pylint: disable=invalid-name
     orpo = "orpo"  # pylint: disable=invalid-name
-    sppo = "sppo"  # pylint: disable=invalid-name
+    sppo = "sppo_hard"  # pylint: disable=invalid-name
 
 
 class ChatTemplate(str, Enum):
diff --git a/src/axolotl/utils/models.py b/src/axolotl/utils/models.py
index f0ae55a73..fc8a67acf 100644
--- a/src/axolotl/utils/models.py
+++ b/src/axolotl/utils/models.py
@@ -791,7 +791,7 @@ def load_model(
         # then the dpo trainer doesn't want the peft model loaded over it, it just wants the lora/peft config
         if (
             cfg.adapter
-            and cfg.rl in ["dpo", "ipo", "kto_pair", "sppo"]
+            and cfg.rl in ["dpo", "ipo", "kto_pair", "sppo_hard"]
             and not cfg.merge_lora
         ):
             _, lora_config = load_lora(model, cfg, inference=False, config_only=True)
diff --git a/src/axolotl/utils/trainer.py b/src/axolotl/utils/trainer.py
index fe1f6e0bd..1a0e55010 100644
--- a/src/axolotl/utils/trainer.py
+++ b/src/axolotl/utils/trainer.py
@@ -438,7 +438,7 @@ def prepare_optim_env(cfg):
 
 
 def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
-    if cfg.rl in ["dpo", "ipo", "kto_pair", "orpo", "sppo"]:
+    if cfg.rl in ["dpo", "ipo", "kto_pair", "orpo", "sppo_hard"]:
         trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer)
         trainer_builder.model_ref = model[1]
         trainer_builder.peft_config = model[2]