lingbot-vla-2-stackcube-ablations

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.

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).