LingBot-VA ARX SFT Teacher — v2 split, step 1500
Video–action world-model (LingBot-VA) fine-tuned on the ARX LIFT R5 dual-arm WAM-OPD task set. This is the transformer of the SFT teacher at optimizer step 1500.
⚠️ This is the v2 diagnostic run, not the contract deliverable. v2 was trained from base on a 160-episode train split (of 199; per-task 40/10, seed 42) specifically to measure overfitting on held-out data. The contract's full-data teacher is the separate v1 (all-199) run. Do not conflate the two: use v2 for the overfitting analysis below; use v1 for any all-data delivery.
Provenance
| Base model | Robbyant/lingbot-va-base @ 5fbd004a61bac17d3764504f09e0b628fc2c0592 |
| Dataset | ylhaichen04/WAM-OPD_4Tasks @ 651338c2… (LeRobot v2.1, 199 ep / 64,417 frames) |
| Train subset | 160 train episodes (39 held out for validation) |
| Temporal profile | video_7p5hz_action_15hz_k2 (K=2, 16 actions/chunk) |
| Action | 14-D absolute joint targets → 30-D layout [14–19,28,21–26,29], q01/q99 |
| Precision / optim | BF16 FSDP, AdamW lr 5e-6, grad clip 2.0, CFG prob 0.1 |
| This checkpoint | step 1500 of a 5000-step schedule |
Held-out validation (39 episodes, teacher-forced denoising loss, 4 draws)
| step | val latent | val action | val total |
|---|---|---|---|
| 1500 (val min) | 0.12730 | 0.005465 | 0.13276 |
| 3000 | 0.13262 | 0.005814 | 0.13843 |
| 5000 | 0.15310 | 0.006829 | 0.15993 |
Overfitting: validation loss bottoms at step 1500 and rises monotonically through 5000 (+~20%). Step 1500 is the validation minimum.
Important caveat on this metric
The validation loss above is teacher-forced, one-step denoising error — the training objective on unseen data. It is an overfitting tripwire, not a task-success ranking. For a world model, low denoising loss does not imply good multi-step rollouts or real-robot success (compounding error is invisible here). Final checkpoint selection requires closed-loop rollout / real-robot evaluation, which for this real-robot dataset is the data owner's call.
Files
transformer/ — diffusers-format WanTransformer3DModel (config.json +
bf16 safetensors). Load with the LingBot-VA stack; switch attn_mode to
torch/flashattn for inference (training used flex).
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