card: RoboCasa Kitchen success-rate table (arm x step), generated from metrics/eval_metrics.json

README.md

@@ -2,6 +2,24 @@
 license: apache-2.0
 tags: [video-world-model, robotics, robocasa, wan2.2, context-pooling, ibot]
 ---
+<!-- KITCHEN-SR:BEGIN -->
+### RoboCasa Kitchen — success rate (24 tasks x 50 episodes = 1200)
+
+| arm | pooling | 20k | 40k | 60k | 80k | 100k |
+|---|---|---|---|---|---|---|
+| `xattn_ffn_L3` | xattn + FFN refine @ L3 | — | — | **61.92%** | **64.50%** | **65.92%** |
+| `xattn_L3` | xattn (no FFN) @ L3 | — | — | **61.42%** | 77.78%<sup>†0/24</sup> | — |
+| `avg_L3` | avg @ L3 | — | — | **60.50%** | — | — |
+
+EMA weights (video DiT + action head), `seed42av`, `N_ENVS=1`, dropcur=1, budget 50 ep/task.
+Per-task numbers and the full protocol: [`metrics/eval_metrics.json`](metrics/eval_metrics.json).
+**†** = partial cell, fewer than 24 tasks at 50 episodes — not comparable with the bolded ones.
+
+Read the arms against each other at the SAME step only: these are single-seed cells, and the
+`N_ENVS=1` render setting is what keeps the EGL buffer-swap contamination at 0.3% of episodes
+(all of them already failures) instead of the 5.9% of render calls seen at `N_ENVS=5`.
+<!-- KITCHEN-SR:END -->
+
 # WAM_DIT4DIT — context pooling on the **finalized B-method** base (RoboCasa kitchen)
 
 Wan2.2-TI2V-5B video DiT + 48-joint action head, `training_mode=joint`. The base is suhyeok's finalized