---
pretty_name: WorldRide research artifacts
license: apache-2.0
tags:
  - world-model
  - video-generation
  - knowledge-distillation
  - reward-forcing
---

# WorldRide model artifacts

This private dataset repository stores the released checkpoints and provenance
for [Rewarded World-Model Distillation](https://github.com/fly0520/rewarded_wm_distillation).
It does not contain the training datasets.

## Released artifacts

| Directory | Purpose | Step | Main file | SHA256 |
|---|---|---:|---|---|
| `ordered_redmd_step1000` | intermediate ordered action + geometry Re-DMD student | 1000 | `model.pt` | `ac5e8a3e99ba4f024d225dd34f34f2c26b3faed3fee08ea4a9ac07d998233776` |
| `ordered_redmd_step2000` | intermediate ordered action + geometry Re-DMD student | 2000 | `model.pt` | `d27ee4f4966e27d79a93bfaf635c6f7fbb18e74f499fc83458733564573808b3` |
| `ordered_redmd_step3000` | final ordered action + geometry Re-DMD student | 3000 | `generator.safetensors` | `7ed0112773727613bf21dc477e57c831f427e5774bd6e93f37844f6d954b0f61` |
| `ordered_redmd_step3000` | full final training container (generator + critic) | 3000 | `model.pt` | `95bff3c1287cc670cbdc56c3ebd1aee6f4bff67f02ac279ee0611e65bf0e7312` |
| `ode_init_step3000` | exact causal ODE initialization used by the final run | 3000 | `model.pt` | `a5fbd37d21194f972b487b1191dcad59fa68bb43be889847efa7c940f1160a5e` |
| `ordered_redmd_no_geometry_step1000` | intermediate action-only Ordered Re-DMD ablation | 1000 | `model.pt` | `8109a5712a5f0ec0b9527a57c5ed72c84e4702de9de9ddc723e20089dba78b4f` |
| `ordered_redmd_no_geometry_step2000` | intermediate action-only Ordered Re-DMD ablation | 2000 | `model.pt` | `28336d5771a70dfb541e2a24c552b4ded24e664720ba11ffdb6fe0f493566da9` |
| `ordered_redmd_no_geometry_step3000` | final action-only Ordered Re-DMD ablation | 3000 | `generator.safetensors` | `ebd22ca83eaf9ec789869f603e145991f52ef26331e8a658c4c6aec35e637e35` |
| `ordered_redmd_no_geometry_step3000` | full ablation training container (generator + critic) | 3000 | `model.pt` | `b9f220138d4b43b41c31cf3820d4d48b69b988c99c54c1f5b744b1f256522118` |
| `ordered_redmd_no_action_geometry_step1000` | intermediate equal-weight multi-anchor DMD ablation | 1000 | `model.pt` | `9e3d123b1da6bfd1465c451f7572a4feeae8d273931377df071d6e63a390b039` |
| `ordered_redmd_no_action_geometry_step2000` | intermediate equal-weight multi-anchor DMD ablation | 2000 | `model.pt` | `dd8f425034b5b6840c5f82fc78edb9eefb7bbe7c0257d124eafb713da2291aa1` |
| `ordered_redmd_no_action_geometry_step3000` | final equal-weight multi-anchor DMD ablation | 3000 | `generator.safetensors` | `567a5c36232cc97e10ea1f2f5e99a0d6f50079e5de21a95c19abe9394f09c21f` |
| `ordered_redmd_no_action_geometry_step3000` | full ablation training container (generator + critic) | 3000 | `model.pt` | `1de8a5e8b2e075e71879ed38cd85d5bcd3d53bae1fb1b75b03b90c225d11f897` |

The final student uses four Stage-1 updates with the frozen timestep schedule
`[1000, 967, 908, 764, 0]`. It is based on the SANA-WM streaming architecture
and requires the corresponding official causal VAE, Gemma text encoder, and
configuration assets. The optional official refiner remains a separate second
stage and is not embedded in these files.

## Inference loading

The preferred inference artifact is the generator-only safetensors file:

```python
from safetensors.torch import load_file

state_dict = load_file("ordered_redmd_step3000/generator.safetensors")
missing, unexpected = generator.load_state_dict(state_dict, strict=True)
assert not missing and not unexpected
```

`generator` here is the SANA-WM streaming generator wrapper constructed from
the released resolved config. The export contains exactly 872 bfloat16 tensors
and was compared tensor-by-tensor with `model.pt["generator"]` before release.

For training-state inspection or continuation, load the full container:

```python
import torch

checkpoint = torch.load(
    "ordered_redmd_step3000/model.pt",
    map_location="cpu",
    weights_only=True,
)
generator.load_state_dict(checkpoint["generator"], strict=True)
assert checkpoint["step"] == 3000
assert checkpoint["denoising_step_list"] == [1000, 967, 908, 764, 0]
```

The full final container also includes a DMD critic; it is not required for
inference. The ODE file is the direct initialization ancestor recorded by the
final run's `parent_ode_import.json`, not the older legacy step-8000 artifact.
The step-1000 and step-2000 files are full generator-plus-critic snapshots from
the same continuous ordered action + geometry Re-DMD lineage as step-3000;
they are neither ODE initialization nor action-only checkpoints.

The `ordered_redmd_no_geometry_step*` lineage is the paired **w/o Geometry**
ablation: action reward and all typed multi-anchor/DMD training budgets are
retained, while geometry is disabled with zero weight. It resumes the same
sealed step-10 action-smoke transaction as the full method. The final receipt
records non-uniform action-derived local-group weights and verifies that every
geometry diagnostic remained zero.

The `ordered_redmd_no_action_geometry_step*` lineage is the paired **w/o Action + Geometry** ablation. It retains HPS feasibility measurement, three typed anchors, candidate branching, DMD replay, and the full multi-anchor compute budget, but both semantic reward channels are disabled. Therefore every detached local-group reward weight is exactly one; HPS is diagnostic and cannot create a preference by itself. This is an equal-weight multi-anchor DMD control, not a zero-cost vanilla-DMD topology.

For a strictly paired continuation, this run resumes the shared action-smoke checkpoint at step 10 (`model.pt` SHA256 `b7defa292ab90770a4ee3998f1c3807c204c1fd8ce973a7afa0761f5a77bb549`) together with both optimizer states and all RNG states. Formal training uses `start_step=10` and `max_steps=3000`, so its reported global step includes 10 action-rewarded smoke updates; it is not a from-scratch pure equal-weight run. The portable final receipt preserves the historical source-stage label `geometry_final`, even though its method variant is `no_action_geometry`. The included calibration measurements document shared lineage but do not produce non-unit reward weights in this ablation.

## Provenance and verification

- final code commit: `72bf6df662df8ff19e8aecc4488b81941b666b16`
- ODE code commit: `eaaa19d561f6bf368dae7669a296c40ba1336ec1`
- SANA-WM model-config SHA256: `fce36e587f200d67c3acfe7b7c336e36b456e271fa01248dce805bd9c49fe25e`
- parent specification SHA256: `0ff66273574d4cfeca98fba2e0b0f3dfa8bc5af96faaa181a5c56e4241b5834d`
- no-geometry ablation code commit: `1aa3d986769fc882a9063d31a0f03081ac4a74f5`
- no-geometry source receipt SHA256 (before portable path sanitization): `3ac69a48d04a710eaa2a9a021cfc204754fd4bc721cdacb8fa091abbda7410c7`
- uploaded portable receipt SHA256: `db7bb51adc67530d468d4f246a2478daf77d08471fce865104e37f121c942911`
- no-action/no-geometry ablation code commit: `34119901287f3ebdf61096696191a46feafd653c`
- no-action/no-geometry common step-10 model SHA256: `b7defa292ab90770a4ee3998f1c3807c204c1fd8ce973a7afa0761f5a77bb549`
- no-action/no-geometry source receipt SHA256 (before portable path sanitization): `3001545d48837d52d30f1a9c4ce463dbf28408e64dfecb6ca35b902c294d7f69`
- no-action/no-geometry uploaded portable receipt SHA256: `e2159b9b9996f0cdf55cbdc76e961549f8db1400f0950664d58514bf91d4dc2a`

`metadata/manifest.json` describes the selection policy. Every uploaded file is
bound by `metadata/checksums.sha256`; the generator export has an additional
tensor-equality receipt beside it. Machine-local paths in metadata are replaced
with portable placeholders.

The repository code is Apache-2.0. Users must also follow the licenses and
access terms of the upstream SANA-WM, text-encoder, VAE, refiner, and evaluation
assets used with these checkpoints.