---
library_name: server-yam
tags:
- robotics
- rl-token
- pi05
- rlt
---

# EmptyDrum/stack-cubes-rlt-token-v1

RL-token autoencoder (RLT Phase 1) for the **stack-cubes** task. A learned
`<rl>` token + transformer encoder/decoder trained to reconstruct the prefix
hidden states of a **frozen** pi05 policy
(`johannesmichalke/stack-cubes-pi05-v1` @ step 030000), so the encoder distills
the VLA's internal state into a single vector `z_rl`.

- **Trainable:** encoder + decoder + `<rl>` token (~409M params)
- **Frozen:** the pi05 backbone (not included here — reload from the policy repo)
- **Demos:** `johannesmichalke/stack-cubes-split`
- **Objective:** masked reconstruction MSE on the prefix embeddings

## Layout

Each `NNNNNN/token_ae.pt` is a checkpoint at that training step
(`trainable_state_dict` = encoder/decoder/`<rl>` weights only) with a sibling
`config.json` describing the token config + training config.

## Diagnostics (calibrated)

What holds up:
- **Task phase / time** is encoded clearly — t-SNE of z_rl shows a smooth
  start→end gradient, and reconstruction MSE is low. The AE is trained to
  reconstruct the prefix embeddings, so it captures whatever dominates their
  variance (visual/task state).
- Occlusion saliency shows z_rl reads from the cube / manipulation region.

What does NOT hold up (do not overclaim):
- **z_rl is a weak outcome predictor.** A frame-level linear probe for
  success/failure reaches only ~0.66 AUC (~0.70 acc vs a 0.75 majority
  baseline). Outcome is only weakly/late decodable (it rises over an episode),
  and success-vs-failure z_rl trajectories are **not** visibly smoother/
  cleaner than each other — contrary to the original RL-token blog's claim on
  its task. This is expected: the objective is reconstruction, not outcome
  prediction.

Trained with the cached-prefix + bf16 path in
[Server-YAM](https://github.com/HalfEmptyDrum/Server-YAM).