diff --git a/src/axolotl/integrations/kd/__init__.py b/src/axolotl/integrations/kd/__init__.py
index b1a990553..65592627e 100644
--- a/src/axolotl/integrations/kd/__init__.py
+++ b/src/axolotl/integrations/kd/__init__.py
@@ -39,7 +39,10 @@ class KDPlugin(BasePlugin):
 
     def get_trainer_cls(self, cfg):
         if cfg.kd_trainer:
-            from .trainer import AxolotlKDTrainer
+            from .trainer import AxolotlKDTrainer, AxolotlOnlineKDTrainer
+
+            if cfg.kd_online_server_base_url:
+                return AxolotlOnlineKDTrainer
 
             return AxolotlKDTrainer
         return None
diff --git a/src/axolotl/integrations/kd/online_chat_template.py b/src/axolotl/integrations/kd/online_chat_template.py
new file mode 100644
index 000000000..400c72a56
--- /dev/null
+++ b/src/axolotl/integrations/kd/online_chat_template.py
@@ -0,0 +1,41 @@
+from axolotl.prompt_strategies.chat_template import ChatTemplateStrategy, StrategyLoader
+from axolotl.prompters import IGNORE_TOKEN_ID
+from axolotl.utils.logging import get_logger
+
+# Configure the logger
+LOG = get_logger(__name__)
+LOG.setLevel("INFO")
+
+
+class ChatTemplateStrategyWithOnlineKD(ChatTemplateStrategy):
+    @property
+    def supports_batched(self) -> bool:
+        # batching doesn't work well for logprob data
+        return False
+
+    def _tokenize_single_prompt(self, prompt):
+        turns = self.get_conversation_thread(prompt)
+        tools = self._get_tools(prompt)
+        input_ids = self.prompter.build_prompt(
+            turns, tools=tools, add_generation_prompt=True
+        )  # type: ignore
+        labels = [IGNORE_TOKEN_ID] * len(input_ids)
+
+        return {
+            "input_ids": input_ids,
+            "prompts": input_ids,
+            "labels": labels,
+            "attention_mask": [1] * len(input_ids),
+        }
+
+
+class OnlineKDStrategyLoader(StrategyLoader):
+    """
+    Load ChatTemplateStrategy with KD support using StrategyLoader.
+    """
+
+    def _get_strategy_cls(self, cfg):
+        return ChatTemplateStrategyWithOnlineKD
+
+
+load = OnlineKDStrategyLoader()
diff --git a/src/axolotl/integrations/kd/trainer.py b/src/axolotl/integrations/kd/trainer.py
index 343d4c6df..cea864357 100644
--- a/src/axolotl/integrations/kd/trainer.py
+++ b/src/axolotl/integrations/kd/trainer.py
@@ -16,6 +16,14 @@
 KD trainer
 """
 
+import os
+from typing import Any, Optional, Union
+
+import requests
+import torch
+from torch import nn
+from transformers import GenerationConfig
+from trl.models import unwrap_model_for_generation
 from typing_extensions import override
 
 from axolotl.core.trainers.base import AxolotlTrainer
@@ -101,3 +109,214 @@ class AxolotlKDTrainer(AxolotlTrainer):
             loss = outputs.loss if hasattr(outputs, "loss") else outputs
 
         return (loss, outputs) if return_outputs else loss
+
+
+class AxolotlOnlineKDTrainer(AxolotlKDTrainer):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.generation_config = GenerationConfig(
+            max_new_tokens=args.max_new_tokens,
+            temperature=args.temperature,
+            do_sample=True,
+            top_k=0,
+            use_cache=False if args.gradient_checkpointing else True,
+            pad_token_id=self.processing_class.pad_token_id,
+        )
+        # Set custom EOS tokens if they are specified by the model's generation
+        # config. This is important for models with the Llama 3 chat template,
+        # which use special tokens <|eot_id|> and <|eom_id|> to mark the end of
+        # turns or messages.
+        if (
+            hasattr(self.model.generation_config, "eos_token_id")
+            and self.model.generation_config.eos_token_id is not None
+        ):
+            self.generation_config.eos_token_id = (
+                self.model.generation_config.eos_token_id
+            )
+
+    @staticmethod
+    def generate_on_policy_outputs(model, inputs, generation_config, pad_token_id=None):
+        # Generate output with respect to the prompt-only
+        generated_outputs = model.generate(
+            input_ids=inputs["prompts"],
+            attention_mask=inputs.get("prompt_attention_mask", None),
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+        )
+
+        # Get the generated token IDs
+        generated_tokens = generated_outputs.sequences
+        # Calculate new attention mask
+        new_attention_mask = torch.ones_like(generated_tokens)
+        new_labels = generated_tokens.clone()
+
+        # If there's pad_token_id, set attention mask to 0 for padding tokens
+        if pad_token_id is not None:
+            new_labels[new_labels == pad_token_id] = -100
+            new_attention_mask[generated_tokens == pad_token_id] = 0
+
+        return generated_tokens, new_attention_mask, new_labels
+
+    def training_step(
+        self,
+        model: nn.Module,
+        inputs: dict[str, Union[torch.Tensor, Any]],
+        num_items_in_batch: Optional[int] = None,
+    ) -> torch.Tensor:
+        """
+        Perform a training step for the Generalized Knowledge Distillation (GKD) model.
+
+        This method implements the on-policy learning approach described in the GKD paper. With probability
+        `self.lmbda`, it generates new responses using the student model, which are then used for training instead of
+        the original inputs.
+        """
+        with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model:
+            new_input_ids, new_attention_mask, new_labels = (
+                self.generate_on_policy_outputs(
+                    unwrapped_model,
+                    inputs,
+                    self.generation_config,
+                    self.processing_class.pad_token_id,
+                )
+            )
+        inputs["input_ids"] = new_input_ids
+        inputs["attention_mask"] = new_attention_mask
+        inputs["labels"] = new_labels
+
+        target_token_ids, target_logprobs, target_mask = self.get_teacher_logprobs(
+            inputs["input_ids"], inputs["labels"]
+        )
+        inputs["target_token_ids"] = target_token_ids
+        inputs["target_logprobs"] = target_logprobs
+        inputs["target_mask"] = target_mask
+
+        loss = super().training_step(model, inputs, num_items_in_batch)
+        return loss
+
+    def get_teacher_logprobs(self, input_ids, labels):
+        request_body = {
+            "model": "arcee-ai/Trinity-Large-Preview",
+            "prompt": input_ids,
+            "logprobs": self.axolotl_cfg.kd_online_topk,
+            "echo": True,
+            "skip_special_tokens": False,
+            "n": 1,
+            "max_tokens": 0,
+            "temperature": 1.0,
+        }
+        base_url = self.args.kd_online_server_base_url
+        api_url = f"{base_url}/v1/completions"
+        bearer_token = os.getenv("OPENAI_API_KEY")
+
+        headers = {"Authorization": f"Bearer {bearer_token}"}
+        response = requests.post(
+            api_url, json=request_body, headers=headers, timeout=30
+        )
+        prompt_logprobs = response.choices[0].logprobs.top_logprobs[
+            1:
+        ]  # prune first null position
+        return self.transform_logprobs(input_ids, labels, prompt_logprobs)
+
+    def transform_logprobs(self, input_ids, labels, logprobs):
+        """
+        Transform logprobs to target format for KD training
+        """
+
+        target_seq_len = len(logprobs)
+        input_seq_len = len(input_ids)
+        input_padding_len = input_seq_len - target_seq_len
+        # get non-zero top-k (prune None logprobs from vllm data step)
+        top_k_vals = [
+            len(logprobs[i])
+            for i in range(len(logprobs))
+            if logprobs[i] is not None and len(logprobs[i])
+        ]
+        max_top_k = max(set(top_k_vals), key=top_k_vals.count)
+        min_top_k = min(set(top_k_vals), key=top_k_vals.count)
+        top_k = min(max_top_k, min_top_k)
+        if top_k == 0:
+            raise ValueError("No non-zero top-k logprobs found.")
+
+        target_logprobs = []
+        target_token_ids = []
+        target_mask = []
+
+        if input_padding_len < 0:
+            # logprobs is longer than target_seq_len,
+            # so we need to slice from the left/beginning of logprobs
+            logprobs = logprobs[:-input_seq_len]
+            input_padding_len = 0
+            # target_seq_len = input_seq_len
+
+        # truncate the second dimension of the logprobs to top_k
+        logprobs = [row[:top_k] for row in logprobs]
+
+        # fill with -inf for padding_len tokens for top_k tokens
+        # extend target_logprobs with a padding_len x top_k 2D list filled with -inf
+
+        # we shift for causal models in the trainer, so start the range from 0
+        for _ in range(0, input_padding_len):
+            target_logprobs.append([-float("inf")] * top_k)
+            target_token_ids.append(list(range(top_k)))
+            target_mask.append([0] * top_k)
+
+        for position in range(input_padding_len, input_seq_len):
+            if labels[position] == -100:
+                target_mask.append([0] * top_k)
+            else:
+                target_mask.append([1] * top_k)
+
+        for _, token_pos_logprobs in enumerate(logprobs):
+            # Initialize collections for logprobs and token_ids
+            position_logprobs = []
+            position_token_ids = []
+
+            # Process each token probability entry
+            for entry in token_pos_logprobs:
+                # Extract logprob value
+                logprob = entry["logprob"]
+
+                # Parse token_id from the "token_id:###" format
+                token_id = int(entry["token"].split(":")[1])
+
+                # Append to our collections
+                position_logprobs.append(logprob)
+                position_token_ids.append(token_id)
+
+            # Convert to a tensor for easier manipulation
+            position_logprobs_tensor = torch.tensor(
+                position_logprobs, dtype=torch.float
+            )
+
+            # Now we have distribution at T1 in log form, i.e. log p_{T1}(k).
+            # Next, re-scale to T2 = self.kd_temperature via exponent-based trick
+            # p_{T2}(k) = [p_{T1}(k)]^(T1 / T2) / Z
+            #
+            # Convert from log to probability
+            teacher_probs_t1 = position_logprobs_tensor.exp()
+            # normalize probabilities to sum to 1 in case they aren't already
+            teacher_probs_t1_sum = teacher_probs_t1.sum(dim=0, keepdim=True)
+            if teacher_probs_t1_sum > 1e-9:
+                teacher_probs_t1 = teacher_probs_t1 / teacher_probs_t1_sum
+            if self.kd_temperature != self.gen_temperature:
+                # Exponentiate by factor (T1 / T2)
+                exponent = self.gen_temperature / self.kd_temperature
+                teacher_probs_t2 = teacher_probs_t1**exponent
+            else:
+                teacher_probs_t2 = teacher_probs_t1
+            # Re-normalize
+            # teacher_probs_t2 = teacher_probs_t2 / teacher_probs_t2.sum(
+            #     dim=0, keepdim=True
+            # )
+            # Convert back to log
+            position_logprobs_tensor = torch.log(teacher_probs_t2)
+
+            # Now we have log p_{teacher, T2}(k) stored in position_logprobs_tensor
+            position_logprobs_scaled = position_logprobs_tensor.tolist()
+
+            target_logprobs.append(position_logprobs_scaled)
+            target_token_ids.append(position_token_ids)
+
+        # Update sample with transformed logprobs
+        return target_token_ids, target_logprobs, target_mask
diff --git a/tests/e2e/utils.py b/tests/e2e/utils.py
index 842cbf118..c0a146221 100644
--- a/tests/e2e/utils.py
+++ b/tests/e2e/utils.py
@@ -179,7 +179,7 @@ def check_tensorboard(
     tag: str,
     lt_val: float,
     assertion_err: str,
-    rtol: float = 0.02,
+    rtol: float = 0.05,
 ) -> None:
     """
     helper function to parse and check tensorboard logs