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tinyvla2/RESULTS.md

@@ -166,3 +166,31 @@ clear negative result. Would need support examples that carry information NOT
 available elsewhere (e.g. from a genuinely different episode/task, or examples
 that vary the dynamics being asked about) to give the model a reason to attend
 to them.
+
+## Demo conditioning v2 — exemplars from a DIFFERENT TASK of the same robot
+Fix attempt for v1's dead channel: sample support pairs cross-task (so they carry
+robot-dynamics info the identity channels don't duplicate). Held-out LeKiwi, no FT:
+
+| condition | endpoint |
+|---|---|
+| zero-floor | 241 mm |
+| GARBAGE demos + desc=none | **260.9 mm** (best) |
+| REAL LeKiwi demos + desc=none | 263.6 mm |
+| OTHER-ROBOT (jaco) demos + desc=none | 265.9 mm |
+| REAL demos + desc=lekiwi | 294.0 mm |
+| GARBAGE demos + desc=lekiwi | 294.2 mm |
+
+TWO findings:
+1. **Absolute level improved a lot**: 261-266mm vs 288-295mm for every prior
+   variant — closest to the 241mm floor in the whole series. But this came from
+   TRAINING WITH the extra (noisy) channel acting as regularization/augmentation,
+   NOT from using demo content.
+2. **The channel is still content-blind**: real (263.6) ≈ garbage (260.9) ≈
+   other-robot (265.9), all within noise. Cross-task sampling did NOT break the
+   shortcut — the model still ignores WHAT is in the exemplars.
+Conclusion: in-context demo conditioning does not work in this architecture even
+with informative exemplars. Its only benefit is incidental regularization. Two
+plausible remaining causes: (a) the mean-pool + 3-step action summary destroys
+the information, (b) a single MLP token per exemplar is too weak a pathway —
+proper in-context learning likely needs the exemplars inside the attention stream
+(e.g. as extra tokens the expert cross-attends over), not squeezed through an MLP.