Xperience-10M 128-Episode Relay and Fine-Tune Plan

This is the executable plan for moving from metadata selection to real multi-episode training. It does not claim model-quality results until data is downloaded, staged, audited, trained, and evaluated on held-out sessions.

Current Preflight

Host	Role	Status
HF-reachable relay host	Dataset download relay	Needs Hugging Face access and enough scratch storage for one batch
Training host	Persistent data + training	Needs enough storage for the staged selection and the training/eval stack

Conclusion: use a Hugging Face reachable machine as the download relay and the training machine as the persistent data store. The current transfer path uses chunked parallel file transfer and overlapping batch prefetch so the relay can transfer the current batch while future batches are downloaded.

Current execution status:

a 128-episode relay job has been launched on an HF-reachable host,
chunked parallel transfer is active for staged files,
overlapping batch prefetch is active for later batches,
no multi-episode model-quality training result is claimed yet.

Selected Data

Selection file: results/omni_finetune/xperience10m_128_episode_selection.json
Download list: results/omni_finetune/xperience10m_128_episode_download_files.txt
Episodes: 128
Sessions: 128 unique sessions
Split: 96 train / 16 val / 16 test
Files: 896 training files
Excluded: visualization.rrd
Estimated training-host storage: 277.71 GiB excluding RRD

Relay Setup

Define host-specific paths outside the public artifact:

export RELAY_WORKDIR=/path/to/ropedia-episode-task-suite
export RELAY_ROOT=/path/to/xperience10m_relay
export TRAINING_HOST=<training-user>@<training-host>
export TRAINING_REPO=/path/to/ropedia-episode-task-suite
export TRAINING_DATA_ROOT=/path/to/xperience10m_128

Create a dedicated relay-to-training SSH key:

ssh <relay-host> 'mkdir -p ~/.ssh && chmod 700 ~/.ssh && test -f ~/.ssh/xperience10m_relay_ed25519 || ssh-keygen -t ed25519 -N "" -f ~/.ssh/xperience10m_relay_ed25519 -C xperience10m-relay-to-training'
ssh <relay-host> 'cat ~/.ssh/xperience10m_relay_ed25519.pub'

Append that public key to the training host ~/.ssh/authorized_keys, then verify from the relay:

ssh <relay-host> 'ssh -i ~/.ssh/xperience10m_relay_ed25519 -o BatchMode=yes -o StrictHostKeyChecking=accept-new <training-user>@<training-host> hostname'

Copy Minimal Repo Files to Relay

ssh <relay-host> 'mkdir -p "$RELAY_WORKDIR"'
rsync -av \
  scripts/omni/relay_xperience10m_selection.py \
  scripts/omni/parallel_chunk_transfer.py \
  results/omni_finetune/xperience10m_128_episode_selection.json \
  <relay-host>:"$RELAY_WORKDIR"/

Relay Dry Run

ssh <relay-host> '
cd "$RELAY_WORKDIR" &&
python3 relay_xperience10m_selection.py \
  --selection-json xperience10m_128_episode_selection.json \
  --relay-root "$RELAY_ROOT" \
  --batch-max-gib 40 \
  --batch-max-episodes 16 \
  --transfer-host "$TRAINING_HOST" \
  --transfer-root "$TRAINING_DATA_ROOT" \
  --ssh-key ~/.ssh/xperience10m_relay_ed25519 \
  --transfer-mode chunked \
  --chunk-parallel 8 \
  --chunk-size-mib 8 \
  --chunk-threshold-mib 8 \
  --delete-after-transfer \
  --dry-run
'

Start Relay

Run in a persistent terminal or tmux session on the relay:

export HF_TOKEN=...
cd "$RELAY_WORKDIR"
python3 relay_xperience10m_selection.py \
  --selection-json xperience10m_128_episode_selection.json \
  --relay-root "$RELAY_ROOT" \
  --batch-max-gib 40 \
  --batch-max-episodes 16 \
  --transfer-host "$TRAINING_HOST" \
  --transfer-root "$TRAINING_DATA_ROOT" \
  --ssh-key ~/.ssh/xperience10m_relay_ed25519 \
  --transfer-mode chunked \
  --chunk-parallel 8 \
  --chunk-size-mib 8 \
  --chunk-threshold-mib 8 \
  --delete-after-transfer

Batch sizing is intentionally conservative. A 40 GiB batch size keeps restarts and partial-transfer cleanup cheaper than treating the full 277.71 GiB selection as one unit. Future batches can be downloaded in a separate prefetch-only relay process after disk headroom is checked.

Training-Host Data Validation

After transfer completes:

cd "$TRAINING_REPO"
python3 scripts/omni/discover_xperience10m_sources.py \
  --workspace "$TRAINING_REPO" \
  --data-root "$TRAINING_DATA_ROOT" \
  --output results/omni_finetune/source_discovery_128.json \
  --report-output results/omni_finetune/DATA_BLOCKER_REPORT_128.md \
  --target-episodes 128 \
  --skip-modelscope \
  --skip-huggingface

Then build the episode manifest:

python3 scripts/omni/build_episode_manifest.py \
  --workspace "$TRAINING_REPO" \
  --data-root "$TRAINING_DATA_ROOT" \
  --max-episodes 128 \
  --train-fraction 0.75 \
  --val-fraction 0.125 \
  --test-fraction 0.125 \
  --split-seed 7 \
  --output results/omni_finetune/episode_manifest_128.json

Content Rebalance Gate

Parse staged annotations before training:

python3 scripts/omni/audit_staged_xperience10m_content.py \
  --data-root "$TRAINING_DATA_ROOT" \
  --selection-json results/omni_finetune/xperience10m_128_episode_selection.json \
  --output-json results/omni_finetune/staged_content_audit_128.json \
  --output-csv results/omni_finetune/staged_content_audit_128.csv \
  --report-output results/omni_finetune/STAGED_CONTENT_AUDIT_128.md

If a category dominates train, val, or test, swap episodes before training.

Training Order

1. Qwen3-Omni LoRA Baseline

Use this as the first real multi-episode SFT run because the repo already has working Qwen3-Omni training/eval scripts.

Expected dataset:

128 episodes
32,768 max windows at 256 windows per episode
held-out sessions in val/test

2. Cosmos3-Nano Compatibility

Cosmos3-Nano should be treated as a second branch:

first run inference compatibility on a few staged clips,
then adapt data format for Cosmos video/action tasks,
then run post-training only after Qwen3-Omni and content audit pass.

Good Cosmos tasks:

video + text -> physical reasoning text,
video + text -> future state/action label,
video + action/text -> future video,
video + text -> action trajectory proxy.

Do not start with Cosmos3-Super. Cosmos3-Nano is the practical first target; Super is for a later run after data format, metrics, and compute are stable.

Acceptance Gates

128 selected episodes staged on the training host.
No visualization.rrd in training data.
128 unique sessions preserved.
Train/val/test session leakage is zero.
Content audit reviewed before training.
Qwen3-Omni eval runs on held-out sessions.
Cosmos3-Nano branch starts with compatibility, not immediate full fine-tune.