diff --git a/src/megatron/bridge/peft/base.py b/src/megatron/bridge/peft/base.py
index f7d18e441f..2379f69f56 100644
--- a/src/megatron/bridge/peft/base.py
+++ b/src/megatron/bridge/peft/base.py
@@ -21,6 +21,7 @@
 import torch.nn as nn
 from megatron.core.transformer.module import MegatronModule
 
+from megatron.bridge.peft.recompute import maybe_enable_recompute_inputs_grad
 from megatron.bridge.peft.walk_utils import walk
 
 
@@ -107,6 +108,9 @@ def __call__(self, model: ModelType, training: bool = True) -> ModelType:
                 model_to_walk = model
             walk(model_to_walk, self.transform)
 
+        if training:
+            maybe_enable_recompute_inputs_grad(model)
+
         if not training:
             self.freeze_model(model, training=training)
 
diff --git a/src/megatron/bridge/peft/recompute.py b/src/megatron/bridge/peft/recompute.py
new file mode 100644
index 0000000000..82aea96884
--- /dev/null
+++ b/src/megatron/bridge/peft/recompute.py
@@ -0,0 +1,123 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Helpers for PEFT-specific activation recompute fixes."""
+
+from __future__ import annotations
+
+from functools import wraps
+from typing import Iterable, Set
+
+import torch
+from megatron.core.utils import unwrap_model
+
+from megatron.bridge.utils.common_utils import print_rank_0
+
+
+PEFT_RECOMPUTE_PATCHED: Set[int] = set()
+
+
+def _iter_unwrapped_models(model) -> Iterable[torch.nn.Module]:
+    """Yield unwrapped Megatron modules regardless of list/list-like inputs."""
+    unwrapped = unwrap_model(model)
+    if isinstance(unwrapped, list):
+        for module in unwrapped:
+            if module is not None:
+                yield module
+    else:
+        if unwrapped is not None:
+            yield unwrapped
+
+
+def maybe_enable_recompute_inputs_grad(model, peft_recompute_patched: Set[int] | None = None) -> Set[int]:
+    """Enable grad on TransformerBlock inputs when only adapters are trainable.
+
+    Root cause analysis:
+
+    - Megatron's CheckpointFunction.backward() is only invoked by PyTorch autograd
+      when at least one input tensor requires grad.
+    - With PP>1, received tensors from other stages have requires_grad=True, so
+      checkpoint backward is always called.
+    - With PP=1 and frozen base model, embedding outputs have requires_grad=False.
+      This means CheckpointFunction.backward() is never called, and LoRA gradients
+      inside the checkpoint are never computed.
+
+    Solution: Hook TransformerBlock.forward to ensure hidden_states.requires_grad=True
+    before it enters checkpointed computation. This doesn't unfreeze any parameters;
+    it just ensures the autograd machinery calls checkpoint's backward.
+
+    Borrowed (with modifications) from
+    https://github.com/HollowMan6/verl/blob/4285f0601028aee7ddcb9ec5a15198ebfc69bba3/verl/utils/megatron_peft_utils.py
+    """
+
+    from megatron.core.transformer.transformer_block import TransformerBlock
+
+    patched_registry = peft_recompute_patched or PEFT_RECOMPUTE_PATCHED
+
+    try:
+        for unwrapped_model in _iter_unwrapped_models(model):
+            cfg = getattr(unwrapped_model, "config", None)
+            if cfg is None or getattr(cfg, "recompute_method", None) is None:
+                continue
+
+            if id(unwrapped_model) in patched_registry:
+                continue
+
+            params = list(unwrapped_model.named_parameters())
+            trainable_adapter = any(p.requires_grad and ".adapter." in n.lower() for n, p in params)
+            trainable_base = any(
+                p.requires_grad and (".to_wrap." not in n.lower() and ".adapter." not in n.lower()) for n, p in params
+            )
+
+            if not (trainable_adapter and not trainable_base):
+                continue  # Not adapter-only training, no fix needed
+
+            def _patch_transformer_block(module: torch.nn.Module) -> bool:
+                if isinstance(module, TransformerBlock):
+                    original_forward = module.forward
+
+                    @wraps(original_forward)
+                    def patched_forward(hidden_states, *args, _original_forward=original_forward, **kwargs):
+                        # Ensure hidden_states requires grad so checkpoint backward is called
+                        if (
+                            torch.is_tensor(hidden_states)
+                            and not hidden_states.requires_grad
+                            and hidden_states.is_floating_point()
+                        ):
+                            hidden_states = hidden_states.detach().requires_grad_(True)
+                        return _original_forward(hidden_states, *args, **kwargs)
+
+                    module.forward = patched_forward
+                    return True
+                return False
+
+            patched = False
+            for module in unwrapped_model.modules():
+                if _patch_transformer_block(module):
+                    patched = True
+            if patched:
+                patched_registry.add(id(unwrapped_model))
+                print_rank_0(
+                    "[PEFT+Recompute] Patched TransformerBlock.forward to enable grad on "
+                    "hidden_states input. This ensures checkpoint backward is called when "
+                    "only adapters are trainable (PP=1 with frozen base model).",
+                )
+    except Exception as exc:  # pragma: no cover - best effort logging
+        # Log but don't fail - user will see grad_norm=0 and can debug
+        print_rank_0(f"[PEFT+Recompute] Warning: Failed to patch TransformerBlock: {exc}")
+
+    return patched_registry
+
+
+__all__ = ["maybe_enable_recompute_inputs_grad", "PEFT_RECOMPUTE_PATCHED"]
diff --git a/tests/unit_tests/peft/test_recompute.py b/tests/unit_tests/peft/test_recompute.py
new file mode 100644
index 0000000000..3742e36ee7
--- /dev/null
+++ b/tests/unit_tests/peft/test_recompute.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Unit tests for PEFT-specific recompute helpers."""
+
+from types import SimpleNamespace
+
+import torch
+
+from megatron.bridge.peft import recompute as recompute_mod
+from megatron.bridge.peft.recompute import maybe_enable_recompute_inputs_grad
+
+
+class DummyAdapter(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.ones(1))
+
+
+class DummyTransformerBlock(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.last_input_requires_grad = None
+
+    def forward(self, hidden_states, *args, **kwargs):
+        self.last_input_requires_grad = hidden_states.requires_grad
+        return hidden_states
+
+
+class DummyModel(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.config = SimpleNamespace(recompute_method="uniform")
+        self.block = DummyTransformerBlock()
+
+        # Frozen base parameter (not trainable)
+        self.base = torch.nn.Linear(1, 1, bias=False)
+        self.base.weight.requires_grad = False
+
+        # Trainable adapter parameter whose name contains ".adapter."
+        # Use a ModuleDict with key "adapter" so that the full parameter
+        # name includes the expected substring (".adapter.") used by
+        # maybe_enable_recompute_inputs_grad.
+        self.adapter = torch.nn.ModuleDict({"adapter": DummyAdapter()})
+
+    def modules(self):
+        for module in super().modules():
+            yield module
+
+
+def _patch_transformer_block(monkeypatch):
+    import megatron.core.transformer.transformer_block as transformer_block
+
+    monkeypatch.setattr(
+        transformer_block,
+        "TransformerBlock",
+        DummyTransformerBlock,
+        raising=False,
+    )
+
+
+def test_maybe_enable_recompute_inputs_grad_patches_block(monkeypatch):
+    _patch_transformer_block(monkeypatch)
+    recompute_mod.PEFT_RECOMPUTE_PATCHED.clear()
+
+    model = DummyModel()
+    patched_registry = maybe_enable_recompute_inputs_grad(model, set())
+
+    assert id(model) in patched_registry
+
+    patched_forward = model.block.forward
+
+    input_tensor = torch.zeros(2, 2)
+    assert input_tensor.requires_grad is False
+
+    model.block(input_tensor)
+    assert model.block.last_input_requires_grad is True
+
+    # Second invocation should be a no-op (no duplicate patch)
+    maybe_enable_recompute_inputs_grad(model, patched_registry)
+    assert model.block.forward is patched_forward