WORL-NVRM

Next-Video Reward Model (NVRM) scores a candidate video continuation in the context of preceding video and a text prompt. It provides a relative preference signal for reinforcement learning of video world models.

Related policy releases: WORL-Helios and WORL-Lingbot.

Model details

Field	Description
Base model	Qwen3-VL-4B-Instruct
Base revision	ebb281ec70b05090aa6165b016eac8ec08e71b17
Architecture	36-layer Qwen3-VL backbone with a one-dimensional reward head
Release format	Merged BF16 backbone and separate scalar head, all in Safetensors
Input	Text prompt, preceding video context and candidate continuation
Output	One unnormalized scalar reward; higher indicates stronger relative preference
Intended use	Video preference research and reward evaluation in video RL

The training LoRA is already merged into the backbone. The reward head is in nvrm_head.safetensors, under key rm_head.weight. This is a reward model, not a text-generation model; loading it as a generic chat model does not produce the documented reward.

Training data and method

NVRM uses pairwise video preferences with a Bradley–Terry objective. The WORL manuscript describes approximately 312.4K preference pairs drawn from Sekai Real Walking, Sekai Game, OmniWorld, Object-centric Revisiting, OpenScene and General categories. Preference construction uses automated multimodal judgments, order-consistency checks and graph-based filtering.

The team reports that initial scene imagery for its self-created OpenScene data was generated with gpt-image. This repository distributes model artifacts; the full preference corpus is not bundled with the weights.

Download and use

hf download Efficient-Large-Model/WORL-NVRM --local-dir artifacts/WORL-NVRM

Use the WORL NVRM reward runtime, which loads the merged Qwen3VLModel backbone and the separate scalar head. The complete chat formatting and preprocessing contract is required; a generic Transformers pipeline is not a replacement for that runtime. The WORL source release is being prepared separately.

Preprocessing / scoring	Release contract
Candidate continuation	5 seconds
Frame sampling	1 FPS
Resize	Height 360
Frame encoding	JPEG quality 85
Image pixel bounds	1,024–76,800
Thinking / hidden-state normalization	Disabled / disabled
Reward	Last-token scalar, raw BF16 logit

The source runtime profile uses Transformers 4.57.6, Accelerate 1.13.0, qwen-vl-utils 0.0.14 and FlashAttention 2.7.4.post1. The artifact does not require trust_remote_code. Pin a Hub revision for reproducible use and verify the files against worl-bundle.json or RELEASE_MANIFEST.json.

Files and provenance

The repository contains three backbone shards, the reward head, tokenizer and processor assets, figures, license notices and integrity manifests.

Weights and model/processor configurations are unchanged from qyoo/NVRM-world at 03f850630c0b. Documentation and release manifests were updated for this repository. RELEASE_MANIFEST.json records the source revision and checksums.

The source release reports matching preprocessing tensors and raw BF16 reward output against its inference fixture. That is a conversion/runtime check, not a measure of general reward quality. This publication adds no new model-quality or hardware benchmark.

Limitations

Rewards are relative and distribution-dependent, not calibrated probabilities. The model can miss physical or temporal errors, learn superficial preferences, and be vulnerable to reward hacking. Biases in the base model, candidate videos and preference judgments can affect rankings. Behavior beyond the documented preprocessing profile is not established by this release.

NVRM is intended for research and comparative reward evaluation. Its output does not establish factual correctness, content safety or suitability for safety-critical decisions.

License

This release retains the source model's Apache License 2.0. Preserve NOTICE.txt when redistributing it. The merged Qwen3-VL backbone is also distributed under Apache-2.0.