---
license: other
tags:
  - robotics
  - video-language-action
  - libero
  - mixture-of-transformers
---

# lingbot-va-mot-posttrain-libero-goal-gradaccum43

Full-parameter post-train of [`lingbot-va-mot`](https://huggingface.co/ZhuoranChen/lingbot-va-mot) on the **LIBERO-Goal** benchmark (10 tasks, original prompts). Base architecture is the Mixture-of-Transformers (MoT) video-action model from [joliachen/lingbot-va](https://github.com/joliachen/lingbot-va) (`model_mot.py`).

## Training config

| | |
|---|---|
| base checkpoint | `checkpoints/lingbot-va-mot` |
| dataset | LIBERO-Goal (`data/libero_goal_lerobot`) |
| `freeze_backbone` | `False` — full-parameter fine-tune, all 10.0B params (30-layer video stream + action stream + embedders/heads) |
| steps | 4000 |
| learning rate | 1e-5 |
| world size | 2 GPUs |
| `gradient_accumulation_steps` | 43 (effective batch = 2 × 43 = 86) |
| `attn_window` | 72 |
| `frame_chunk_size` | 4 |
| `guidance_scale` / `action_guidance_scale` | 5.0 / 1.0 |
| `num_inference_steps` / `action_num_inference_steps` | 25 / 50 |
| `snr_shift` / `action_snr_shift` | 5.0 / 0.05 |
| action representation | 30-dim padded vector; native 7-dim LIBERO OSC action (xyz + euler + gripper) in slots 0–6, rest zero-padded |
| observation cameras | `agentview_rgb`, `eye_in_hand_rgb` |

## Architecture note: action stream width

This checkpoint (and its `lingbot-va-mot` base) uses a **no-bottleneck** MoT design: the action stream runs at the full video-stream width **d_v = 3072** end-to-end (`action_embedder`: Linear(30 → 3072), `action_proj_out`: Linear(3072 → 30), and every per-block action module — `action_attn1/2`, `action_ffn`, `action_norm2`, `action_scale_shift_table` — is shape-identical to its video-stream counterpart). This differs from the lingbot-va **paper**, which describes the action stream operating through a narrower **768-dim bottleneck** (30 → 768 → … → 768 → 30) rather than the full 3072-dim width used here. See [`lingbot-va-mot`'s model card](https://huggingface.co/ZhuoranChen/lingbot-va-mot) for how these action-stream weights were initialized before this fine-tune.

## LIBERO-Goal closed-loop evaluation (checkpoint step 4000)

10 episodes/task, closed-loop rollout in the LIBERO-Goal env:

| Task | Success |
|---|---|
| open the middle drawer of the cabinet | 9/10 |
| put the bowl on the stove | 10/10 |
| put the wine bottle on top of the cabinet | 9/10 |
| open the top drawer and put the bowl inside | 10/10 |
| put the bowl on top of the cabinet | 8/10 |
| push the plate to the front of the stove | 10/10 |
| put the cream cheese in the bowl | 10/10 |
| turn on the stove | 10/10 |
| put the bowl on the plate | 10/10 |
| put the wine bottle on the rack | 10/10 |
| **Total** | **96/100 (96.0%)** |

## Repo contents

Only `transformer/` (the fine-tuned weights) is included here. The original checkpoint directory also symlinks `text_encoder/` (`google/umt5-xxl`), `tokenizer/`, and `vae/` (Wan2.1 `AutoencoderKLWan`) from the shared base checkpoint — those are unchanged stock components and are not duplicated in this repo. Load them from [`ZhuoranChen/lingbot-va-mot`](https://huggingface.co/ZhuoranChen/lingbot-va-mot) or the public Wan2.1 release when using this checkpoint standalone.