LingBot-VA LIBERO-Goal — Step 4000
LingBot-VA fine-tuned on LIBERO-Goal for 4,000 steps. This release contains the transformer weights and configuration.
Evaluation
The checkpoint obtained 96.4% success (482/500) on LIBERO-Goal: 50 rollouts for each of the 10 tasks. Evaluation used 20 video denoising steps and 50 action denoising steps.
| Task ID | Successes | Episodes | Success rate |
|---|---|---|---|
| 0 | 50 | 50 | 100% |
| 1 | 50 | 50 | 100% |
| 2 | 38 | 50 | 76% |
| 3 | 49 | 50 | 98% |
| 4 | 49 | 50 | 98% |
| 5 | 50 | 50 | 100% |
| 6 | 50 | 50 | 100% |
| 7 | 50 | 50 | 100% |
| 8 | 50 | 50 | 100% |
| 9 | 46 | 50 | 92% |
Training
433 demonstrations, two 128×128 camera views, and 4,000 updates on 4 × H200. Full configuration and training recipe.
Epoch estimate (provisional)
If all 433 demonstrations each contribute exactly one valid training segment,
the sample-exposure estimate is approximately 1,108.55 equivalent fine-tuning
epochs: 4,000 optimizer updates × 120 effective global batch size / 433.
The run-specific valid-segment list is not included, so this number remains
conditional. Distributed-sampler padding can also make the actual DataLoader
pass count differ. This estimate excludes base-model pretraining; see
TRAINING.md for the reported batch configuration.
Loading
Download this repository and replace the base model's transformer/
directory with the transformer/ directory from this checkpoint while
retaining the base model's tokenizer/, text_encoder/, and vae/.
Limitations
Evaluation covers LIBERO-Goal only. This is a multi-step LingBot-VA model, not a one-step Flash-WAM distillation.