---
license: apache-2.0
library_name: transformers
tags: [robotics, vla, lingbot-vla, lerobot, manipulation]
---

# LingBot-VLA 2.0 — Stack-the-cubes ablations

Finetunes of **LingBot-VLA 2.0** (6B, MoE action expert) on Unitree G1 Dex1 dual-arm
cube stacking. All runs freeze the vision encoder; distillation teachers stay frozen,
current+future depth/DINO query heads train. Muon, lr 1e-4, L1_fm, bounds_99_woclip,
absolute actions.

| Subfolder | Data | Action space | Steps | Note |
|---|---|---|---|---|
| `joint100/`    | 100ep | joint (14 arm + 2 grip) | 20000 (0.42 epoch) | vision frozen, 2xA100 |
| `eef/`         | 150ep (v1+v2) | EEF pose (14 + 2 grip) | 20000 (0.29 epoch) | vision frozen, 2xA100 |
| `eef100/`      | 100ep | EEF pose (14 + 2 grip) | 20000 (0.42 epoch) | vision frozen, 2xA100 |
| `eef100_10ep/` | 100ep | EEF pose (14 + 2 grip) | 59979 (**10 full epochs**) | vision frozen, GBS=16, 2xB200 |

Ablation axes: `joint100` vs `eef100` = joint vs EEF (same data); `eef` vs `eef100`
= mixed 150ep vs clean 100ep; `eef100` vs `eef100_10ep` = training exposure
(0.42 epoch vs 10 full epochs of the same 100ep EEF data — the original runs never
completed even one full pass over their training set). Each `*/global_step_*/`
holds deployable HF weights (`model-0000x-of-00006.safetensors` + tokenizer/config).
EEF models output end-effector poses — the client must IK them back to joints
(see g1-client `main_eef.py`).