| # Training method and reproduction |
|
|
| E5 adds public state history as two tokens (stride 50) and an executed-action summary |
| of the last eight commands; visual history is disabled. Action/state history masks remain |
| independent. Public state history means prior drive-target observations at policy decisions, |
| not measured qpos. See `scripts/e5_rollout_history.py` and the history implementations under |
| `third_party/lingbot-vla-v2/lingbotvla/`. |
|
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| C3 augmentation is enabled at probability 0.8; camera keys receive independent draws, |
| while temporal frames share parameters, maps and noise per camera. Photometric perturbations |
| and probability-0.5 Fourier amplitude jitter are enabled; background/style are disabled. |
| Current and future depth/video teacher pixels share the augmented images, with no cached teacher |
| features. The exact parameters are in `configs/augmentation/c3_v1.json` and actual configs. |
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| Both runs use 2500 clean trajectories, 549787 frames, no holdout or validation selection, |
| fresh official baseline initialization, seed 7, Muon, mixed BF16 computation and FP32 master |
| and exported checkpoint parameters. Configs retain the actual LR schedule, FSDP2, action/state |
| sizes, teacher loss weights, history settings, normalization and dependency layout. |
|
|
| | Run | Epochs | Steps | GPUs | Microbatch | Accumulation | Global batch | LR | Minimum LR | |
| |---|---:|---:|---:|---:|---:|---:|---:|---:| |
| | ep2 | 2 | 17180 | 4 | 8 | 2 | 64 | 0.0001 | 5e-05 | |
| | ep6 | 6 | 17178 | 8 | 12 | 2 | 192 | 0.00015 | 7.5e-05 | |
|
|
| Install the original runtime requirements from `third_party/lingbot-vla-v2/` into a compatible |
| CUDA environment. Place the official base in `checkpoints/base/lingbot-vla-v2-6b`, Qwen3-VL |
| and MoGe dependencies in the paths specified by the configs, and the original RoboTwin LeRobot |
| trajectory directories under `data/lerobot`. Training manifests include both original train/val |
| directories intentionally: the actual all-clean runs have no held-out validation split. |
| These assets are dependencies and are not included in this Git repository. |
|
|
| Run from `third_party/lingbot-vla-v2` so `../../` config references resolve to repository root. |
| Set `MASTER_ADDR`, `MASTER_PORT`, `NODE_RANK` and `PHASE4_NODE_LOCAL_ROOT` from the allocated |
| training environment. For epoch2 use NNODES=1, NPROC_PER_NODE=4 and NODE_RANK=0; epoch6 uses |
| NNODES=2 and NPROC_PER_NODE=4 on each node. `TRAIN_CONFIG` is `../../configs/ep2_training.yaml` |
| or `../../configs/ep6_training.yaml`; `RANK_ENTRY` is `../../deployment/finetune/e5_rank_entry.py` |
| or `../../deployment/finetune/e5_8gpu_rank_entry.py` respectively. |
|
|
| ```bash |
| export PHASE4_HF_EXPORT_DTYPE=fp32 |
| export PHASE4_STATE_HISTORY_REQUIRED=1 |
| export PHASE4_ACTION_HISTORY_REQUIRED=1 |
| export PHASE4_VALIDATION_CACHE=0 |
| export PHASE4_HF_DEDUP=0 |
| export PHASE4_PERF_MODE=0 |
| export PHASE4_DEPTH_TEACHER_BATCH=0 |
| python -m torch.distributed.run --nnodes="$NNODES" --nproc-per-node="$NPROC_PER_NODE" --node-rank="$NODE_RANK" --master-addr="$MASTER_ADDR" --master-port="$MASTER_PORT" --max-restarts=0 "$RANK_ENTRY" tasks/vla/train_lingbotvla.py "$TRAIN_CONFIG" |
| ``` |
|
|
| This portable invocation exposes the original training entry and recipe. It has not been |
| rerun for this release and does not recreate the original scheduler, data staging or recovery |
| orchestration. The actual run completion records report zero automatic restarts. Use the |
| actual sanitized configs, not similarly named generic upstream examples. |
|
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| Final training losses and elapsed training seconds are reported in `RESULTS.json`. |
|  |
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| The plots show each actual optimizer-step loss and a labeled trailing 100-step mean (shorter |
| prefix window at the beginning). The rank0 metric records contain total loss all-reduced |
| over the FSDP group. Scalar-only CSVs are in `loss/ep2.csv` and `loss/ep6.csv`; coverage and |
| original record SHA256 values are in `loss/summary.json`. Both series cover every optimizer |
| step without gaps. Raw logs and all non-scalar metadata remain local: 原始记录保存在本地。 |
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|
