diff --git a/param_decomp_lab/tests/test_three_pool_grad_scaling.py b/param_decomp_lab/tests/test_three_pool_grad_scaling.py
new file mode 100644
index 000000000..ac4abd81b
--- /dev/null
+++ b/param_decomp_lab/tests/test_three_pool_grad_scaling.py
@@ -0,0 +1,52 @@
+"""Regression test for PPGD -> CI gradient scaling under a multi-rank CI pool.
+
+The CI pool ends each step with an AVG all-reduce over its ``n_ci`` ranks
+(``all_reduce_ci_fn_grads``). PPGD's CI grad is injected per-position on a single
+CI rank, so that AVG divides it by ``n_ci``; ``_scale_grads`` must pre-multiply
+the CI grad by ``n_ci`` to compensate (the LW stoch path does the same via its
+``/ n_ci`` denom). V/U never hits that AVG, so it keeps the plain scale.
+
+At ``n_ci=1`` the factor is a no-op — which is why the bug was invisible to the
+8-GPU (``n_ci=1``) configs and only bit production (``n_ci`` = 16 / 24).
+"""
+
+from types import SimpleNamespace
+from typing import cast
+
+import torch
+
+from param_decomp_lab.three_pool.context import PPGDContext
+from param_decomp_lab.three_pool.runtime import _ThreePoolRuntime
+from param_decomp_lab.three_pool.step_ppgd import RawGrads, _scale_grads
+
+
+def _fake_ctx(*, n_ci: int, n_ppgd: int) -> PPGDContext:
+    # _scale_grads only reads ctx.world.{n_ci,n_ppgd}; cast through object.
+    fake = SimpleNamespace(world=SimpleNamespace(n_ci=n_ci, n_ppgd=n_ppgd))
+    return cast(PPGDContext, cast(object, fake))
+
+
+def _fake_cfg(*, coeff_ppgd: float) -> _ThreePoolRuntime:
+    # _scale_grads only reads cfg.coeff_ppgd; cast through object.
+    return cast(_ThreePoolRuntime, cast(object, SimpleNamespace(coeff_ppgd=coeff_ppgd)))
+
+
+def _ones() -> dict[str, torch.Tensor]:
+    return {"site": torch.ones(2, 2)}
+
+
+def test_ppgd_ci_grad_carries_extra_n_ci_factor() -> None:
+    n_ci, n_ppgd, n_examples_local, coeff = 4, 2, 8, 3.0
+    raw = RawGrads(v=_ones(), u=_ones(), ci=_ones(), sources=_ones())
+
+    _scale_grads(
+        raw, n_examples_local, _fake_ctx(n_ci=n_ci, n_ppgd=n_ppgd), _fake_cfg(coeff_ppgd=coeff)
+    )
+
+    vu_scale = coeff / (n_examples_local * n_ppgd)
+    torch.testing.assert_close(raw.v["site"], torch.full((2, 2), vu_scale))
+    torch.testing.assert_close(raw.u["site"], torch.full((2, 2), vu_scale))
+    # CI must carry the extra * n_ci to survive the CI-pool AVG all-reduce.
+    torch.testing.assert_close(raw.ci["site"], torch.full((2, 2), vu_scale * n_ci))
+    # Sources: 1 / n_examples_local only — no coeff, no 1/n_ppgd, no n_ci.
+    torch.testing.assert_close(raw.sources["site"], torch.full((2, 2), 1.0 / n_examples_local))
diff --git a/param_decomp_lab/three_pool/step_ppgd.py b/param_decomp_lab/three_pool/step_ppgd.py
index 4b66ef005..b7a18af52 100644
--- a/param_decomp_lab/three_pool/step_ppgd.py
+++ b/param_decomp_lab/three_pool/step_ppgd.py
@@ -27,8 +27,8 @@
   D4. Recon loss with the refined sources (the (N+1)'th forward).
   D5. Backward: one ``torch.autograd.grad`` over the UNSCALED recon sum yields
       raw g_VU + g_CI + g_sources; each consumer's own normalization is applied
-      explicitly afterward (V/U + CI: coeff_ppgd / n_examples_global; sources:
-      1 / n_examples_local — no coeff, no 1/n_ppgd).
+      explicitly afterward (V/U: coeff_ppgd / n_examples_global; CI: that times
+      n_ci to survive the CI-pool AVG-reduce; sources: 1 / n_examples_local).
   D5b. Send g_CI to CI pool (every rank, on its batch slice). Peer-to-peer
        point-to-point — no PPGD-internal reduce needed, so fires immediately
        after backward to unblock CI's recv-wait sooner.
@@ -232,20 +232,27 @@ def _scale_grads(
     only its batch-slice's contribution, so the per-rank scale carries the
     1/n_ppgd_ranks and the in-pool SUM-reduce reassembles the full-batch grad.
 
+    V/U and CI share that per-rank scale but diverge by their downstream
+    reduction: V/U is SUM-reduced in the PPGD pool, but the CI grad is later
+    AVG-reduced over n_ci ranks on the CI pool (``all_reduce_ci_fn_grads``), which
+    divides it by n_ci. CI therefore needs an extra * n_ci to survive that AVG —
+    mirroring the LW stoch path's `/ n_ci` denom.
+
     Sources are per-rank-local adversary state optimized against THIS rank's own
-    recon mean (recon_sum_loss / n_examples_local) — no coeff, no 1/n_ppgd
-    (those are V/U-reduction artifacts). Identical to the warmup source grad.
+    recon mean (recon_sum_loss / n_examples_local) — no coeff, no 1/n_ppgd, no
+    n_ci. Identical to the warmup source grad.
 
-    Folding any one consumer's scaling into the differentiated scalar — as an
-    earlier version did, dividing by n_ppgd for the V/U reduce — silently
-    mis-scales the others (it gave the source step a spurious 1/n_ppgd).
+    Folding any one consumer's scaling into the differentiated scalar silently
+    mis-scales the others, which is why each is applied explicitly here.
     """
     n_ppgd_ranks = ctx.world.n_ppgd
-    vu_and_ci_grad_scale = cfg.coeff_ppgd / (n_examples_local * n_ppgd_ranks)
+    n_ci = ctx.world.n_ci
+    vu_grad_scale = cfg.coeff_ppgd / (n_examples_local * n_ppgd_ranks)
+    ci_grad_scale = vu_grad_scale * n_ci
     source_grad_scale = 1.0 / n_examples_local
-    _scale_grads_in_place(raw.v, vu_and_ci_grad_scale)
-    _scale_grads_in_place(raw.u, vu_and_ci_grad_scale)
-    _scale_grads_in_place(raw.ci, vu_and_ci_grad_scale)
+    _scale_grads_in_place(raw.v, vu_grad_scale)
+    _scale_grads_in_place(raw.u, vu_grad_scale)
+    _scale_grads_in_place(raw.ci, ci_grad_scale)
     _scale_grads_in_place(raw.sources, source_grad_scale)