luca0621/appgen-sft-ngc-v1

AppGen NGC SFT v1

Portable, hash-audited MS-SWIFT training data for the NGC-F ablation (plus NGC-VT only when that optional arm is present in this revision). Images are procedurally rendered AppGen screenshots from the same 50 training environments used to construct the SFT rows. They are not real-device or user screenshots. No public benchmark, AndroidWorld evaluation, held-out evaluation, or third-party benchmark images are included.

Config	Arm	Family	Coordinates	Exposures
ngc-f-qwen25-absolute	ngc-f	qwen25	absolute-pixel	3588
ngc-f-qwen3-normalized	ngc-f	qwen3	normalized-1000	3588

Each config contains 2,768 canonical navigation rows, 400 direct-grounding rows, and 420 completion-boundary replay exposures (3,588 total; 3,168 unique semantic examples). NGC-F uses action-only grounding targets: the assistant target is a single <tool_call> and contains no grounding <think>. Canonical navigation and completion replay rows retain their original <think> + <tool_call> targets.

source=appgen_direct_grounding_v2 identifies direct-grounding rows. Navigation and completion rows retain the canonical navigation source tag; completion duplicates are explicitly identified by ngcf_v1_replay_role (penultimate_action or success_terminate).

Qwen2.5 coordinates are absolute screenshot pixels. Qwen3 coordinates are normalized to the integer 0--1000 frame. The two family configs are paired by semantic ID and differ only where the family coordinate/tool contract requires it.

Training reference

The corresponding NGC-F run starts from the original Qwen Instruct backbone, uses one epoch, global batch 32, learning rate 5e-7, cosine scheduling with 5% warmup, bf16, ZeRO-2, seed 42, and full LLM tuning while freezing the vision encoder and multimodal aligner. The manifest records 113 optimizer updates. These are provenance/reference settings rather than loader defaults.

Provenance, privacy, and licensing

The screenshots are generated HTML-to-PNG AppGen training-environment renders with synthetic task content; no real accounts, private conversations, or captured user/device data are intentionally included. Packaging is fail-closed: every image must be a PNG inside its row's generated environment and under the allowed training root, while benchmark/evaluation path tokens are rejected. Images are content-addressed and deduplicated across family configs.

This dataset is published with license: other; redistribution/use remains subject to the AppGen project and any applicable asset terms. It does not redistribute model weights. source_data/ preserves the exact original MS-SWIFT JSON bytes for audit purposes; those archival files contain original cluster paths and should not be used directly on another machine.

Images use portable images/sha256/... paths and are deduplicated across all configs in this revision. bundle_manifest.json pins every data/image/source-contract hash. To recover a local MS-SWIFT JSON array:

python materialize_ms_swift.py \
  --bundle-root . \
  --config ngc-f-qwen25-absolute \
  --output /data/appgen/qwen25_ngc_f_v1.json