feat(g05): train with LeRobot language recipes

docs/source/language_and_recipes.mdx

@@ -146,6 +146,31 @@ The renderer does not apply a tokenizer chat template. Policy processors decide
 Blend recipes select one weighted sub-recipe deterministically from the sample index.
 `recipes/subtask_mem.yaml` trains the compact core blend — high-level subtask prediction, low-level execution, and memory. `recipes/subtask_mem_vqa_speech.yaml` is the fuller variant that also adds VQA and spoken interjection responses.
 
+Annotation-dependent blends can set `select_from_applicable: true` and declare
+`requires` on each component. The renderer first removes components whose
+required bindings resolve to `None`, then performs the deterministic weighted
+selection. This matches mixed-CoT policies where unavailable annotation formats
+must not consume probability:
+
+```yaml
+select_from_applicable: true
+blend:
+  subtask:
+    weight: 2
+    requires: [subtask]
+    messages:
+      - {
+          role: assistant,
+          content: "${subtask}",
+          stream: low_level,
+          target: true,
+        }
+  action:
+    weight: 1
+    messages:
+      - { role: user, content: "${task}", stream: low_level }
+```
+
 A message recipe with a supervised assistant turn on the `low_level` stream trains
 the π0.5 paper's joint sequence instead of a blend: the target span gets text CE
 while also conditioning the action losses in the same forward.