diff --git a/docs/commands.md b/docs/commands.md
index 1ffb836..749f0d8 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -232,7 +232,7 @@ soup --no-audit-log <cmd> / SOUP_NO_AUDIT_LOG=1  Opt out of the per-command audi
 soup eval unlearning <run-id> --benchmark tofu|muse|wmdp  Forget Quality + Model Utility + PrivLeak verdict
 soup edit set --base <m> --method rome|memit|alphaedit|grace --subject "..." --target "..." [--output <dir>] [--device cpu] [--governor/--no-governor] [--registry-id <id>] [--cov-corpus <jsonl|txt>]  Live surgical knowledge edit (GPT-2 Conv1D + Llama; --cov-corpus = covariance-preconditioned ROME, rome-only; --plan-only available)
 soup edit diff <before-run> <after-run> --probes p.jsonl [--before-model <m> --after-model <m>]  Knowledge-injection diff (live before/after generation when both models given)
-soup train --task unlearn  NPO/SimNPO/RMU unlearning from data.forget_set (+ optional data.retain_set)
+soup train  # task: unlearn  NPO/SimNPO/RMU unlearning from data.forget_set (+ optional data.retain_set)
 soup train  # data.format='raft'  Answer-only span-mask RAFT training (golden+distractor docs, [doc-N] citations); generator-stage configs auto-link the latest RA-DIT retriever
 soup ra-dit --retriever-config <r.yaml> --generator-config <g.yaml> [--retriever-model <m>] [--plan-only]  One-shot two-stage RA-DIT: train retriever → record pairing → train generator
 soup eval citation <data> [--style bracket|inline|footnote] [--shuffle-seed N] [--output o.json]  Citation precision/recall/F1 over predictions or RAFT rows
diff --git a/docs/training.md b/docs/training.md
index d39c8da..2fddf9b 100644
--- a/docs/training.md
+++ b/docs/training.md
@@ -210,7 +210,7 @@ soup train --config unlearn.yaml --yes
 soup eval unlearning <run-id> --benchmark tofu --evidence evidence.json --output report.json
 ```
 
-`soup train --task unlearn` is live (v0.71.9): it loads a LoRA-wrapped policy, a frozen reference copy (NPO / RMU), and the forget / retain JSONL sets, then optimises the per-method loss — NPO's `(2/β)·mean(-logσ(-β·(π_logp − ref_logp)))` drives the policy's forget-set log-prob below the reference (= forgetting), while the retain set anchors capability. Run NPO/SimNPO **with** a `retain_set` — without one the policy has no utility anchor and Soup warns loudly.
+`task: unlearn` is live (v0.71.9): it loads a LoRA-wrapped policy, a frozen reference copy (NPO / RMU), and the forget / retain JSONL sets, then optimises the per-method loss — NPO's `(2/β)·mean(-logσ(-β·(π_logp − ref_logp)))` drives the policy's forget-set log-prob below the reference (= forgetting), while the retain set anchors capability. Run NPO/SimNPO **with** a `retain_set` — without one the policy has no utility anchor and Soup warns loudly.
 
 Three orthogonal axes: **Forget Quality** (pre/post forget-loss delta), **Model Utility** (retain-accuracy preserved), **PrivLeak** (membership-inference AUC distance from 0.5). Bundled mini-fixtures for all three benchmarks ship in the box (v0.71.1 added MUSE + WMDP alongside the existing TOFU set), so `--benchmark muse|wmdp` runs without supplying evidence. The WMDP forget-set probes ship **redacted** (placeholder prompts + `REFUSED` responses) — Soup never bundles verbatim hazardous-knowledge content.