diff --git a/tests/distributed/_chunked_nll_allgather_worker.py b/tests/distributed/_chunked_nll_allgather_worker.py
new file mode 100644
index 00000000000..8dac7594e25
--- /dev/null
+++ b/tests/distributed/_chunked_nll_allgather_worker.py
@@ -0,0 +1,187 @@
+# Copyright 2020-2026 The HuggingFace Team. 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.
+
+"""Companion worker for the chunked-NLL FSDP2 all-gather perf-regression test.
+
+Launched under ``accelerate launch --config_file <fsdp2_reshard>`` by ``test_distributed.py``. It runs a single SFT
+``chunked_nll`` training step on an FSDP2-sharded tiny model and counts how many all-gather collectives occur during
+that step, so a regression that re-gathers ``lm_head.weight`` once per token chunk (the PR #6077 failure mode — correct
+loss, silently slow) is caught by a bounded assertion.
+
+``_chunked_cross_entropy_loss`` chunks over *valid tokens* (``for start in range(0, n_valid, chunk_size)``), so the
+regression scales with ``ceil(n_valid / chunk_size)`` and only shows up when more than one token chunk runs. The zen
+test data is tiny, so this worker shrinks the chunk size (see ``_TEST_CHUNK_SIZE``) to force many token chunks, and
+derives the regression threshold from the exact ``n_valid`` captured from inside the loss path — never from vocab size.
+
+Why count real collectives and not ``DTensor.full_tensor()``: under FSDP2 the parameter unshard is driven by autograd
+pre-hooks / c10d collectives, not by explicit ``full_tensor()`` calls, so a ``full_tensor`` counter is blind to it. We
+use ``CommDebugMode`` (``torch.distributed.tensor.debug``) — torch's purpose-built, DTensor-native comm counter, which
+records ``funcol.all_gather_into_tensor`` and the ``c10d`` ``_allgather_base_`` / ``allgather_`` variants that FSDP2
+emits. (An earlier version also ran a hand-rolled ``TorchDispatchMode``, but re-dispatching sharded ops from a custom
+mode under FSDP2 mismatches the index/weight devices on the embedding lookup, so we rely on ``CommDebugMode`` alone.)
+
+Prints one machine-parseable line ``CHUNKED_NLL_ALLGATHER_RESULT {json}`` that the pytest side asserts on.
+Self-contained on purpose: it imports only public TRL symbols and runs as ``__main__`` under ``accelerate launch``.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import tempfile
+
+from datasets import load_dataset
+
+from trl import SFTConfig, SFTTrainer
+
+
+MODEL_ID = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
+RESULT_PREFIX = "CHUNKED_NLL_ALLGATHER_RESULT"
+
+
+def _count_all_gathers(comm_counts: dict) -> int:
+    """Sum the all-gather collectives from a ``CommDebugMode.get_comm_counts()`` dict.
+
+    ``CommDebugMode`` keys the dict by the comm op (funcol / c10d). FSDP2's parameter unshard shows up as
+    ``funcol.all_gather_into_tensor`` (and the ``_allgather_base_`` / ``allgather_`` c10d variants), so we match on the
+    op's string name containing ``all_gather`` / ``allgather`` and total those. This is the DTensor-native counter — it
+    observes the autograd-hook-driven gathers that ``DTensor.full_tensor()`` is blind to, without a hand-rolled
+    ``TorchDispatchMode`` (which mis-dispatches sharded ops under FSDP2).
+    """
+    total = 0
+    for op, n in comm_counts.items():
+        name = str(op).lower()
+        if "all_gather" in name or "allgather" in name:
+            total += int(n)
+    return total
+
+
+class _MeasuringSFTTrainer(SFTTrainer):
+    """SFTTrainer that counts all-gather collectives during its first ``training_step``.
+
+    The measurement must run *inside* the real ``trainer.train()`` loop, not by calling ``training_step`` directly:
+    under ``fsdp_cpu_ram_efficient_loading`` the model is on CPU/meta until ``_inner_training_loop`` FSDP-wraps it and
+    moves it to GPU. Calling ``training_step`` on ``trainer.model`` before ``train()`` runs the embedding lookup with a
+    CPU weight against a CUDA input → device-mismatch crash. Overriding ``training_step`` lets the trainer do all
+    wrapping/placement, while we wrap the (single, since ``max_steps=1``) step in ``CommDebugMode`` to tally the FSDP2
+    unshard collectives.
+    """
+
+    comm_counts: dict | None = None
+
+    def training_step(self, *args, **kwargs):
+        from torch.distributed.tensor.debug import CommDebugMode
+
+        # Only measure the first step (with max_steps=1 there is exactly one); guard anyway so the counts
+        # reflect a single step even if the caller raises max_steps later.
+        if self.comm_counts is not None:
+            return super().training_step(*args, **kwargs)
+        comm_mode = CommDebugMode()
+        with comm_mode:
+            loss = super().training_step(*args, **kwargs)
+        self.comm_counts = comm_mode.get_comm_counts()
+        return loss
+
+
+# The chunked-CE loop chunks over *valid tokens*, not vocab: `for start in range(0, n_valid, chunk_size)`
+# in `_chunked_cross_entropy_loss`. So a per-chunk `lm_head.weight` re-gather regression scales with
+# ceil(n_valid / chunk_size) — the TOKEN-chunk count — and is only observable when more than one chunk
+# runs (n_valid > chunk_size). The zen test data is tiny (~120 valid tokens total), so with the default
+# chunk size of 256 only a single chunk would run and a regression would be invisible. We therefore shrink
+# the chunk size for this test so the tiny batch genuinely exercises many token-chunks.
+_TEST_CHUNK_SIZE = 4
+
+
+def main() -> None:
+    import trl.trainer.sft_trainer as sft
+
+    # Shrink the chunk size BEFORE the trainer patches the lm_head (it reads this module constant at
+    # construction). With ~120 valid tokens this yields ~30 token-chunks, so a per-chunk re-gather
+    # regression would do ~30 lm_head all-gathers vs O(1) for the fixed path — a wide, detectable margin.
+    sft._CHUNKED_LM_HEAD_CHUNK_SIZE = _TEST_CHUNK_SIZE
+
+    # Capture the real valid-token count from inside the chunked-CE path, so the regression threshold is
+    # derived from the exact n_valid the loop iterates over (never guessed from token lengths).
+    captured = {}
+    _orig_cce = sft._chunked_cross_entropy_loss
+
+    def _capturing_cce(hidden_states, lm_head_weight, chunk_size, *args, **kwargs):
+        out = _orig_cce(hidden_states, lm_head_weight, chunk_size, *args, **kwargs)
+        # Returns (loss, correct, entropy_sum, n_valid_tensor); n_valid is the 4th element.
+        captured["n_valid"] = int(out[3].item())
+        captured["chunk_size"] = int(chunk_size)
+        return out
+
+    sft._chunked_cross_entropy_loss = _capturing_cce
+
+    dataset = load_dataset("trl-internal-testing/zen", "standard_language_modeling", split="train")
+    # Write trainer artifacts to a throwaway temp dir so the worker leaves no state in the repo checkout and
+    # repeated runs can't collide. tempfile keeps this self-contained (no reliance on the launch cwd).
+    tmp_out = tempfile.mkdtemp(prefix="chunked_nll_fsdp2_")
+    args = SFTConfig(
+        output_dir=tmp_out,
+        loss_type="chunked_nll",
+        # Pack as many of the tiny examples into the single measured step as possible, so n_valid is well
+        # above the (shrunk) chunk size and the token-chunk count is large.
+        per_device_train_batch_size=8,
+        max_length=64,
+        max_steps=1,
+        report_to="none",
+        bf16=True,
+    )
+    trainer = _MeasuringSFTTrainer(model=MODEL_ID, args=args, train_dataset=dataset)
+
+    vocab_size = trainer.model.config.vocab_size
+
+    # Run the real training loop: it FSDP-wraps the model and moves it to GPU, then calls training_step
+    # once (max_steps=1), which our subclass measures under CommDebugMode.
+    trainer.train()
+
+    comm_counts = trainer.comm_counts or {}
+    all_gathers = _count_all_gathers(comm_counts)
+    comm_total = sum(int(n) for n in comm_counts.values())
+
+    n_valid = captured.get("n_valid", 0)
+    chunk_size = captured.get("chunk_size", _TEST_CHUNK_SIZE)
+    n_chunks = -(-n_valid // chunk_size) if n_valid else 0  # ceil(n_valid / chunk_size) — TOKEN chunks
+
+    last = trainer.state.log_history[-1] if trainer.state.log_history else {}
+    train_loss = last.get("train_loss")
+
+    result = {
+        "vocab_size": int(vocab_size),
+        "n_valid": int(n_valid),
+        "chunk_size": int(chunk_size),
+        "n_chunks_if_regressed": int(n_chunks),
+        "all_gathers": int(all_gathers),
+        "commdebug_total": int(comm_total),
+        "loss_finite": train_loss is not None and math.isfinite(train_loss),
+    }
+    if trainer.accelerator.is_main_process:
+        print(f"{RESULT_PREFIX} {json.dumps(result)}", flush=True)  # noqa: T201 - result channel for the launcher
+
+
+if __name__ == "__main__":
+    # Print the full traceback from this worker directly: when `accelerate launch` re-raises a child
+    # failure, the parent only sees a truncated `CompletedProcess` repr, which hides the real error frame.
+    # Surfacing it here puts the complete traceback in the worker's own stderr (and thus the CI log).
+    import sys
+    import traceback
+
+    try:
+        main()
+    except Exception:
+        traceback.print_exc()
+        sys.stderr.flush()
+        raise
diff --git a/tests/distributed/data/accelerate_configs/fsdp2_reshard.yaml b/tests/distributed/data/accelerate_configs/fsdp2_reshard.yaml
new file mode 100644
index 00000000000..13a31f2cf2b
--- /dev/null
+++ b/tests/distributed/data/accelerate_configs/fsdp2_reshard.yaml
@@ -0,0 +1,31 @@
+# 2-process FSDP2 config for the chunked-NLL all-gather perf-regression test.
+#
+# `fsdp_reshard_after_forward: true` is the load-bearing setting: it is the condition under which PR
+# #6077's per-chunk `lm_head.weight` all-gather manifested (the param is resharded after forward, then
+# re-gathered during the chunked backward). The minimal `fsdp2.yaml` leaves this at FSDP's default, so a
+# dedicated config that states it explicitly keeps the collective count meaningful. Mirrors
+# `examples/accelerate_configs/fsdp2.yaml` with `num_processes: 2` for a 2-GPU node.
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_activation_checkpointing: false
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_offload_params: false
+  fsdp_reshard_after_forward: true
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_version: 2
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 2
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
diff --git a/tests/distributed/test_distributed.py b/tests/distributed/test_distributed.py
index 2e99bf0f4ea..c5caa9a6142 100644
--- a/tests/distributed/test_distributed.py
+++ b/tests/distributed/test_distributed.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
 import os
 import subprocess
 from pathlib import Path
@@ -376,3 +377,61 @@ def test_grpo_liger(self, config, get_config_path):
             os.environ.copy(),
         )
         # fmt: on
+
+    def test_sft_chunked_nll_fsdp2_no_per_chunk_allgather(self, lazy_shared_datadir):
+        # Perf-regression guard for the PR #6077 class: a chunked cross-entropy path must NOT re-gather the
+        # sharded `lm_head.weight` once per token chunk under FSDP2 (correct loss, silently slow, invisible
+        # to a pass/fail test). The companion worker runs one SFT `chunked_nll` step under a 2-process FSDP2
+        # group (reshard_after_forward=True — the condition that triggers the bug) and counts the all-gather
+        # collectives during that step; here we assert the count stays O(1), not O(n_valid / chunk_size).
+        #
+        # `_chunked_cross_entropy_loss` chunks over VALID TOKENS, not vocab, so the regression scales with
+        # ceil(n_valid / chunk_size) and only manifests when more than one token chunk runs. The worker
+        # shrinks the chunk size so the tiny zen batch exercises many token chunks, and reports the exact
+        # n_valid / chunk_size it measured so this side can both bound the count and confirm the test is
+        # non-vacuous (n_chunks_if_regressed > 1 — otherwise a regression could never have been observed).
+        #
+        # Counting real collectives is required: under FSDP2 the parameter unshard is driven by autograd
+        # hooks / c10d collectives, not by `DTensor.full_tensor()`, so the worker counts the actual
+        # all-gather collectives via CommDebugMode (torch's DTensor-native comm counter) for the step.
+        worker = Path(__file__).parent / "_chunked_nll_allgather_worker.py"
+        config_path = lazy_shared_datadir / "accelerate_configs" / "fsdp2_reshard.yaml"
+        # Pin the repo root onto PYTHONPATH for the child: `accelerate launch` re-execs each rank via
+        # torch.distributed.elastic, which sets sys.path[0] to the launched script's directory, not cwd.
+        # Without this, a non-editable `trl` already in site-packages would shadow the working tree.
+        env = os.environ.copy()
+        env["PYTHONPATH"] = os.pathsep.join([str(ROOT), env.get("PYTHONPATH", "")]).rstrip(os.pathsep)
+        result = subprocess.run(
+            ["accelerate", "launch", "--config_file", str(config_path), str(worker)],
+            env=env,
+            cwd=ROOT,
+            capture_output=True,
+            text=True,
+        )
+        assert result.returncode == 0, f"worker failed:\nSTDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
+
+        prefix = "CHUNKED_NLL_ALLGATHER_RESULT"
+        lines = [ln for ln in result.stdout.splitlines() if ln.startswith(prefix)]
+        assert len(lines) == 1, f"expected exactly one result line, got {lines}\n{result.stdout}"
+        measured = json.loads(lines[0][len(prefix) :].strip())
+
+        assert measured["loss_finite"], f"chunked_nll loss not finite under FSDP2: {measured}"
+        # Non-vacuity guard (the heart of this test): a per-token-chunk regression can only be detected if the
+        # step actually ran multiple token chunks. If only one chunk ran, a regression would gather exactly
+        # once too, so the test would pass for the wrong reason. Require a comfortably multi-chunk run.
+        assert measured["n_chunks_if_regressed"] > 4, (
+            f"test is vacuous — only {measured['n_chunks_if_regressed']} token chunk(s) ran, so a per-chunk "
+            f"regression could not be observed; increase batch/length or shrink chunk_size: {measured}"
+        )
+        # A per-token-chunk-regather regression would do ~n_chunks all-gathers of lm_head.weight in the step;
+        # the fixed path does O(1). `all_gathers` is the total all-gather collective count for the step,
+        # measured by CommDebugMode (the DTensor-native counter that sees FSDP2's autograd-hook-driven
+        # gathers). It legitimately includes one gather per sharded parameter (a handful of decoder layers),
+        # so bound it well below the regression count rather than at exactly 1. The ceiling scales off
+        # n_chunks (never a hardcoded collective count) so it tracks the model's token/chunk arithmetic.
+        observed = measured["all_gathers"]
+        ceiling = max(16, measured["n_chunks_if_regressed"] // 4)
+        assert observed < measured["n_chunks_if_regressed"], (
+            f"per-chunk lm_head.weight all-gathers detected (#6077 regression): {measured}"
+        )
+        assert observed <= ceiling, f"unexpectedly many all-gathers (possible regression): {measured}"