BridgeQuant-Async — LIBERO-Goal 95.0%
Full-finetuned 480M VLA (LFM2.5-VL-450M backbone + BridgeConnector + BridgeActionHead). Best LIBERO-Goal checkpoint of the campaign.
| suite | this repo | GR00T N1.7 (2B, 12.8M samples) |
|---|---|---|
| Goal | 95.0 | 97.50 |
| Object | 100.0 (separate repo) | 98.45 |
| Spatial | 98.0 (separate repo) | 97.65 |
| Long | 94.0 (separate repo) | 94.35 |
| mean | 96.75 | 97.00 |
Protocol: seed 42, 200 episodes/suite (20 per task × 10 tasks), synchronous rollout,
n_action_steps=8, one Euler denoising step, raw (non-EMA) bundle.
Recipe
train_mode: finetune, lr 5e-5, 30k steps, batch 16, vision 512px, GR00T-style
augmentation, taps [0, 6, 12, 15], 64 role queries, memory rank 256, chunk 16,
flow matching with Beta(1.5, 1) time sampling, num_steps 1.
Goal-specific: transition-balanced sampling (transition_oversample: 1.5,
transition_window: 12) — frames within 12 steps of a debounced gripper
open↔close change are sampled 1.5× more often. No auxiliary objective, no
architecture change; it only reshapes the data distribution of the same flow
loss. Worth +4.5 points on Goal, concentrated in the multi-phase tasks (t06
15→19, t09 16→20).
Async deployment
The same weights run under the asynchronous runtime (cached bridge latent,
~5 ms replan) at 94.0 with one fixed controller — K=8,
--context-refresh-every 2, --precision-refresh --precision-refresh-approach --precision-refresh-uncertainty — no per-suite tuning.
Files
model.safetensors (trainable weights incl. the finetuned backbone),
norm_stats.json, config.json, bridgequant_vla.yaml (config saved with the
checkpoint), train_config.yaml (the exact training YAML).
Eval
uv run python scripts/eval_libero_object.py \
--config train_config.yaml --checkpoint <this-dir> \
--n-action-steps 8 --episodes-per-task 20
The eval suite must match the training suite or success rate is 0%.
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