Shi-Dong · Sep 23, 2026 · Sep 23, 2026 · Sep 23, 2026 · Sep 23, 2026 · Sep 23, 2026
diff --git a/docs/docs.json b/docs/docs.json
@@ -269,6 +269,7 @@
        "examples/infra-features/low-precision",
        "examples/infra-features/p2p-weight-transfer",
        "examples/infra-features/random-async",
+       "examples/infra-features/score-centering",
        "examples/infra-features/train-infer-mismatch-helper",
        "examples/infra-features/true-on-policy"
       ]

diff --git a/docs/examples/index.md b/docs/examples/index.md
diff --git a/docs/examples/infra-features/score-centering.md b/docs/examples/infra-features/score-centering.md
diff --git a/examples/README.md b/examples/README.md
@@ -25,6 +25,7 @@ data and weights around.
 - **[low_precision](./infra_features/low_precision)**: Examples of FP8 training and inference, plus INT4 QAT, for improved throughput and stability.
 - **[p2p_weight_transfer](./infra_features/p2p_weight_transfer)**: Point-to-point weight transfer between training and rollout engines.
 - **[random_async](./infra_features/random_async)**: Dataset-free stress test of the async rollout ↔ trainer loop.
+- **[score_centering](./infra_features/score_centering)**: Center off-policy policy gradients using the sampler's top-k probabilities, with optional TIS or MIS weights.
 - **[train_infer_mismatch_helper](./infra_features/train_infer_mismatch_helper)**: Algorithmic methods for rollout correction (e.g., TIS, MIS).
 - **[true_on_policy](./infra_features/true_on_policy)**: Ensures strictly equal log probabilities between inference (SGLang) and training engines.