diff --git a/docs/source/libero.mdx b/docs/source/libero.mdx
index b95af1d27..6dba5c62e 100644
--- a/docs/source/libero.mdx
+++ b/docs/source/libero.mdx
@@ -114,38 +114,58 @@ LIBERO supports two control modes — `relative` (default) and `absolute`. Diffe
 
 ### Recommended evaluation episodes
 
-For reproducible benchmarking, use **10 episodes per task** across all four standard suites (Spatial, Object, Goal, Long). This gives 400 total episodes and matches the protocol used for published results.
+For reproducible benchmarking, use **10 episodes per task** across all four standard suites (Spatial, Object, Goal, Long). This gives 400 total episodes and matches the protocol used for published results. Success rates may vary by a few percent across evaluation seeds, so we recommend averaging over 3 seeds.
+
+<Tip>
+  To compare two policies on the same episodes, use the same `--seed`, keep
+  `--env.init_states=true`, and run each task in a single batch
+  (`--eval.batch_size` equal to episodes per task).
+</Tip>
 
 ## Training
 
 ### Dataset
 
-We provide a preprocessed LIBERO dataset fully compatible with LeRobot:
+Two preprocessed LIBERO datasets are fully compatible with LeRobot. They contain the same demonstrations with the same schema and differ in how camera frames are stored:
 
-- [HuggingFaceVLA/libero](https://huggingface.co/datasets/HuggingFaceVLA/libero)
+|                           | [lerobot/libero](https://huggingface.co/datasets/lerobot/libero) | [HuggingFaceVLA/libero](https://huggingface.co/datasets/HuggingFaceVLA/libero) |
+| ------------------------- | ---------------------------------------------------------------- | ------------------------------------------------------------------------------ |
+| episodes / frames / tasks | 1,693 / 273,465 / 40                                             | 1,693 / 273,465 / 40                                                           |
+| cameras                   | 2× 256×256×3                                                     | 2× 256×256×3                                                                   |
+| state / action dims       | 8 / 7                                                            | 8 / 7                                                                          |
+| dataset format            | v3.0                                                             | v3.0                                                                           |
+| camera encoding           | MP4 video                                                        | PNG in parquet                                                                 |
+| download size             | **1.9 GB**                                                       | 69.9 GB                                                                        |
+| extra dependency          | video backend (`torchcodec` or `pyav`)                           | none                                                                           |
+
+**We recommend [lerobot/libero](https://huggingface.co/datasets/lerobot/libero)**: **37× smaller download** with **equivalent loading speed** (~330 samples/s per worker). Video re-encoding is slightly lossy; use the image-based variant if you cannot install a video decoding backend.
 
 For reference, the original dataset published by Physical Intelligence:
 
 - [physical-intelligence/libero](https://huggingface.co/datasets/physical-intelligence/libero)
 
+<Tip>
+Pin `--dataset.revision=<commit-sha>` when reporting results — Hub datasets can be re-uploaded, and success rates are only comparable against the same data revision.
+</Tip>
+
 ### Example training command
 
+Train SmolVLA on the recommended dataset:
+
 ```bash
 lerobot-train \
   --policy.type=smolvla \
-  --policy.repo_id=${HF_USER}/libero-test \
   --policy.load_vlm_weights=true \
-  --dataset.repo_id=HuggingFaceVLA/libero \
-  --env.type=libero \
-  --env.task=libero_10 \
-  --output_dir=./outputs/ \
+  --policy.push_to_hub=false \
+  --dataset.repo_id=lerobot/libero \
+  --dataset.video_backend=torchcodec \
+  --output_dir=./outputs/libero_smolvla \
   --steps=100000 \
-  --batch_size=4 \
-  --eval.batch_size=1 \
-  --eval.n_episodes=1 \
-  --env_eval_freq=1000
+  --batch_size=64
 ```
 
+To share the result on the Hub, replace `--policy.push_to_hub=false` with `--policy.repo_id=${HF_USER}/libero-smolvla`. Evaluate saved checkpoints with `lerobot-eval` as shown in the [Evaluation](#evaluation) section.
+
 ## Reproducing published results
 
 We reproduce the results of Pi0.5 on the LIBERO benchmark. We take the Physical Intelligence LIBERO base model (`pi05_libero`) and finetune for an additional 6k steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).