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.

Object-specific: a small language-contrastive auxiliary (in-batch InfoNCE, weight 0.05, pooled z_bridge against frozen text anchors). This suite is the one place where an extra shaping objective helps: plain full finetune scores 93.0 here, langnce takes it to 100.0 (+7.0) and transition oversampling to 98.5 (+5.5). Elsewhere the same signals cost points — on LIBERO-Long the InfoNCE objective drops the suite to 55.0. The useful amount of extra shaping scales with how much slack the latent still has: Object's tasks are single-phase with visually distinct targets, so there is capacity to spend; Long's are multi-phase and every added constraint evicts a rare skill.

Async deployment

Same weights under the asynchronous runtime (cached bridge latent, ~5 ms replan): 100.0 with --context-refresh-every 4, and 99.0 under the unified controller shared by all four suites (K=8, refresh 2, --precision-refresh --precision-refresh-approach --precision-refresh-uncertainty).

Eval

bash

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%.

twanghcmut/bridgequant-async-libero-object-100

BridgeQuant-Async — LIBERO-Object 100.0%

Recipe

Async deployment

Eval

twanghcmut/
bridgequant-async-libero-object-100