---
license: other
library_name: bagel-mot
language:
- en
tags:
- geoug
- geometry
- supervised-fine-tuning
---

# GeoUG-ColdStart

[Project page](https://yangr116.github.io/geoug-project-page/) ·
[Paper](https://yangr116.github.io/geoug-project-page/assets/geoug-paper.pdf)

**The supervised cold-start checkpoint for GeoUG spatial-policy RL.**

GeoUG-ColdStart is the supervised initialization checkpoint for spatial-policy
reinforcement learning. It provides the starting point for training GeoUG-Spatial.

| Property | Value |
|---|---|
| Stage | Interleaved reasoning supervised fine-tuning |
| Weights | EMA cold-start checkpoint |
| Precision | Original BF16 weights |
| Downstream model | GeoUG-Spatial after spatial-policy RL |
| Bundled transform | Matches reasoning SFT and the spatial RL recipe |

## Download

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="rayruiyang/GeoUG-ColdStart",
    local_dir="ckpt/geoug-coldstart",
)
```

These are custom BAGEL/MoT weights. Use the GeoUG runtime rather than a generic
`AutoModel.from_pretrained` call. The weight repository contains the checkpoint
assets; inference and training code live in the GeoUG code checkout.

Verify the download:

```bash
(cd ckpt/geoug-coldstart && sha256sum -c checksums.sha256)
```

## Initialize RL training

Use the GeoUG checkout's separate RL environment, described in
`docs/RL_TRAINING.md`. After downloading this model and
[train_geoug_rl.parquet](https://huggingface.co/datasets/rayruiyang/GeoUG_data/blob/main/train_geoug_rl.parquet),
restore the public training layout:

```bash
python scripts/pack_data_parquet.py unpack-rl \
  --input /absolute/path/to/train_geoug_rl.parquet \
  --output /absolute/path/to/rl_train

export MODEL_PATH="$PWD/ckpt/geoug-coldstart"
export RL_TRANSFORM_CONFIG="$MODEL_PATH/transform_config.yaml"
export RL_DATASET=/absolute/path/to/rl_train
export DEPTH_CACHE_DIR=/absolute/path/to/rl_train/depth-cache
```

The parquet includes training images, camera metadata, and generated-depth
caches. Prepare your own validation/evaluation inputs as required by the RL
recipe; the internal RL evaluation subset is not distributed.

The transform uses multiscale disparity normalization with
`c: [2.0, 8.0, 32.0]`. The RL loader uses `ema.safetensors`, matching the
initialization used by the released recipe.

## Scope and interpretation

This repository provides the initialization checkpoint, not the final adaptive
policy. Use GeoUG-Spatial for the final model. Its reported RL benchmark scores
must not be attributed to GeoUG-ColdStart.

The cold start **model** is released here. The cold start **training data** is a
separate, pending release; it is not included in this repository or in the
800-sample RL training parquet.

## Model family

| Checkpoint | Intended use |
|---|---|
| [GeoUG-Spatial](https://huggingface.co/rayruiyang/GeoUG-Spatial) | Final adaptive spatial reasoning policy |
| [GeoUG-Depth](https://huggingface.co/rayruiyang/GeoUG-Depth) | Standalone metric-depth generation |
| [GeoUG-ColdStart](https://huggingface.co/rayruiyang/GeoUG-ColdStart) | SFT initialization for spatial-policy RL |

## Package and integrity

| File | Contents |
|---|---|
| `ema.safetensors` | BF16 EMA model weights, approximately 27.21 GiB |
| `ae.safetensors` | VAE weights |
| `llm_config.json`, `vit_config.json`, `config.json`, `generation_config.json` | Model and generation configuration |
| `tokenizer.json`, `tokenizer_config.json`, `vocab.json`, `merges.txt` | Tokenizer assets |
| `transform_config.yaml` | Image/depth preprocessing and normalization |
| `checksums.sha256` | SHA-256 checksums for the package files |

Download the complete repository. The release uses EMA weights and does not
require a separate non-EMA checkpoint or a shard index.

## Checkpoint provenance

This is the EMA checkpoint at step 1500 of the v6 interleaved reasoning SFT
run. The original SFT stage starts from the v5 step-1000 initialization. These
training identifiers are retained for experiment traceability.

## License and attribution

GeoUG builds on the BAGEL/MoT architecture and its pretrained components.
This checkpoint accompanies the mixed-license GeoUG code release. Consult
`LICENSE` and `THIRD_PARTY_NOTICES.md` in the GeoUG code checkout for the
applicable component terms, including the CC BY-NC restrictions on the listed
BAGEL/DiT-derived code files. This card does not assign a new blanket license
to upstream components. The data repositories document their own source and
usage terms separately.

## Contact

For questions about this release, contact
[rayyang0116@connect.hku.hk](mailto:rayyang0116@connect.hku.hk).