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Add Florence-Bridge code and handoff documentation
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Source inventory and authority

Authority order

  1. Server run artifacts and full configs on jlu66.
  2. This corrected handoff package.
  3. Public development repositories: ReTime_source and OPTR-StarVLA.

Server facts override stale prose or inferred settings in a repository snapshot.

Server-authoritative origins

  • Development tree: /home/miaochengyu/starVLA-infoflow-codex
  • Curated source tree: /home/miaochengyu/OPTR-StarVLA-clean
  • 20K parent: /mnt/miaochengyu/starvla-infoflow-runtime/runs/florence-scratch-official-100k/florence-scratch-uniform-seed-42-official100k-mb4-eval1k-20260726
  • Confirmatory arms: /mnt/miaochengyu/starvla-infoflow-runtime/runs/infoflow-confirmatory-22k-20260730
  • Frozen proposal: /mnt/miaochengyu/starvla-infoflow-runtime/runs/infoflow-proposals/scratch20k_residual_grid8_gate42f_batch1_20260729/residual_temporal_proposal.json
  • Formal aggregate: /home/miaochengyu/starVLA-infoflow-codex/logs/bridgert/infoflow_confirmatory_eval/seed_117465/final_test_policy_seeds_20p5/aggregate_summary.json

Corrections applied during handoff

  • Corrected DeepSpeed from a stale ZeRO-3 description to the actually used ZeRO-2 configuration.
  • Corrected diffusion_model_cfg.cross_attention_dim from stale 2048 to the actually used 1024.
  • Fixed world size directly at 2 in the portable Accelerate config.
  • Restricted the public launcher to the uploaded Uniform and Residual arms.
  • Removed engineering checksum/path gates from launch.
  • Kept the real semantic identity requirements for data, episodes, valid anchors and proposal support.

Key implementation files

  • starVLA/training/train_starvla.py: trainer, exact-state resume, checkpointing.
  • starVLA/training/infoflow.py: InfoFlow/importance-correction integration.
  • starVLA/dataloader/temporal_proposal.py: proposal parsing and support semantics.
  • starVLA/dataloader/infoflow_sampler.py: traceable sampling.
  • starVLA/scoring/flow_residual.py: residual scoring.
  • starVLA/scoring/residual_cache.py: score-cache handling.
  • scripts/run_florence_bridge_arm.sh: portable two-GPU continuation launcher.
  • scripts/evaluate_bridge_384_no_video.sh: locked 384-rollout orchestrator.

The public dataset and base Florence model are not redistributed in this package. Their local paths are supplied through environment variables.