diff --git a/examples/llm_finetune/nemotron/llama3_3_nemotron_super_49B_squad_peft.yaml b/examples/llm_finetune/nemotron/llama3_3_nemotron_super_49B_squad_peft.yaml
index feb2828dda..359686eee4 100644
--- a/examples/llm_finetune/nemotron/llama3_3_nemotron_super_49B_squad_peft.yaml
+++ b/examples/llm_finetune/nemotron/llama3_3_nemotron_super_49B_squad_peft.yaml
@@ -29,7 +29,7 @@ step_scheduler:
 
 dist_env:
   backend: nccl
-  timeout_minutes: 1
+  timeout_minutes: 20
 
 rng:
   _target_: nemo_automodel.components.training.rng.StatefulRNG
@@ -113,6 +113,7 @@ ci:
   recipe_owner: HuiyingLi
   checkpoint_robustness:
     hf_kl_threshold: 5e-3
+    resume_loss_threshold: 5e-2
     trust_remote_code: true
     distributed.tp_size: 2
     tokenizer_name: nvidia/Llama-3_3-Nemotron-Super-49B-v1_5
diff --git a/tests/functional_tests/checkpoint_robustness/test_checkpoint_robustness_llm.py b/tests/functional_tests/checkpoint_robustness/test_checkpoint_robustness_llm.py
index b2ca493f34..7dbeae938a 100644
--- a/tests/functional_tests/checkpoint_robustness/test_checkpoint_robustness_llm.py
+++ b/tests/functional_tests/checkpoint_robustness/test_checkpoint_robustness_llm.py
@@ -35,6 +35,10 @@
 import torch.nn.functional as F
 from torch.distributed.tensor import DTensor
 
+from nemo_automodel.components.checkpoint.checkpointing import (
+    _MODELS_REQUIRING_BUFFER_REINIT,
+    _reinit_non_persistent_buffers,
+)
 from nemo_automodel.components.config._arg_parser import parse_args_and_load_config
 from nemo_automodel.recipes.llm.train_ft import TrainFinetuneRecipeForNextTokenPrediction
 
@@ -214,6 +218,31 @@ def _get_logits(model, input_ids, device, trainer=None) -> torch.Tensor:
         return logits.float().cpu()
 
 
+def _reinit_rotary_per_module(model, default_device):
+    """Recompute DeciLM / Gemma3 style non-persistent rotary buffers on each
+    module's own device.
+
+    HF `from_pretrained` in transformers 5.x leaves ``inv_freq`` uninitialized
+    for models whose rotary buffers are computed in ``__init__`` and never
+    saved to the state dict (e.g. nemotron-nas, gemma3). With
+    ``device_map='auto'`` each rotary module can live on a different GPU, so
+    we drive the recompute per-module using its own inv_freq device rather
+    than a single fixed device.
+    """
+    model_type = getattr(model.config, "model_type", None)
+    if model_type not in _MODELS_REQUIRING_BUFFER_REINIT:
+        return model
+    for mod in model.modules():
+        inv = getattr(mod, "inv_freq", None)
+        if inv is None:
+            continue
+        mod_device = inv.device
+        if mod_device.type == "meta":
+            mod_device = next((p.device for p in mod.parameters()), default_device)
+        _reinit_non_persistent_buffers(mod, mod_device, model_type=model_type)
+    return model
+
+
 def _fix_meta_rotary_embeddings(model):
     """Re-materialize RotaryEmbedding tensors stuck on meta device.
 
@@ -370,6 +399,7 @@ def test_checkpoint_robustness():
                 base_model = _fix_meta_rotary_embeddings(
                     AutoModelForCausalLM.from_pretrained(original_pretrained_path, **hf_kwargs)
                 ).to(device)
+            _reinit_rotary_per_module(base_model, device)
             peft_model = PeftModel.from_pretrained(base_model, str(ckpt_step_dir / "model"))
             hf_logits = _get_logits(peft_model, input_ids, device)
 
@@ -396,6 +426,7 @@ def test_checkpoint_robustness():
                 hf_model = _fix_meta_rotary_embeddings(
                     AutoModelForCausalLM.from_pretrained(str(consolidated_dir), **hf_kwargs)
                 ).to(device)
+            _reinit_rotary_per_module(hf_model, device)
             hf_logits = _get_logits(hf_model, input_ids, device)
             del hf_model