Reinforcement Learning
Transformers
Safetensors
qwen3_omni_moe
text-to-audio
qwen3-omni
audio
video
multimodal
temporal-grounding
dense-video-captioning
Instructions to use Apryle/AVTime-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apryle/AVTime-RL with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Apryle/AVTime-RL") model = AutoModelForMultimodalLM.from_pretrained("Apryle/AVTime-RL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen3-Omni-30B-A3B-Instruct | |
| library_name: transformers | |
| tags: | |
| - qwen3-omni | |
| - audio | |
| - video | |
| - multimodal | |
| - temporal-grounding | |
| - dense-video-captioning | |
| - reinforcement-learning | |
| # AVTime-RL | |
| `AVTime-RL-BiTSC-GRPO-v1` is a merged, bf16 milestone for time-aware | |
| audio-video understanding. It starts from `Qwen/Qwen3-Omni-30B-A3B-Instruct`, | |
| uses one-round AVTime/LongVALE supervision, and is then optimized with | |
| multi-task GRPO for timestamped dense video captioning, temporal video | |
| grounding, and segment captioning. | |
| ## Milestone identity | |
| | Field | Value | | |
| | --- | --- | | |
| | Release | AVTime-RL-BiTSC-GRPO-v1 | | |
| | Checkpoint | merged checkpoint 60 | | |
| | Precision | bfloat16 | | |
| | Serialization | 15 safetensors shards, about 66 GB total | | |
| | Architecture | `Qwen3OmniMoeForConditionalGeneration` | | |
| | Release date | 2026-08-11 | | |
| This is a fully merged checkpoint; no LoRA adapter needs to be applied at | |
| inference time. Use a recent `transformers` or vLLM build that supports | |
| Qwen3-Omni MoE models and provide both video and audio when available. | |
| ## Training | |
| The GRPO mixture contains 12,484 examples: 70.0% dense video captioning (DVC), | |
| 15.0% temporal video grounding (TVG), and 15.0% segment captioning (SC). | |
| It combines 7,239 LongVALE DVC examples, 1,500 AVTime DVC examples, 1,872 | |
| LongVALE TVG examples, and 1,873 LongVALE SC examples. | |
| The main DVC reward is BiTSC (bidirectional time-semantic consistency): a | |
| local RLAIF judge matches captions to events, while deterministic code measures | |
| the resulting temporal overlap in both Time-to-Event and Event-to-Time | |
| directions. TVG uses official-format parsing plus temporal IoU and boundary | |
| proximity; SC uses deterministic lexical coverage and length calibration. | |
| ## LongVALE evaluation | |
| The following results use the LongVALE test split (1,171 videos and 13,867 | |
| events) and the official metric implementation. | |
| | Task | Metric | Score | | |
| | --- | ---: | ---: | | |
| | Omni-TVG | R@0.3 | 26.13 | | |
| | Omni-TVG | R@0.5 | 16.85 | | |
| | Omni-TVG | R@0.7 | 8.71 | | |
| | Omni-TVG | mIoU | 17.76 | | |
| | Omni-SC | BLEU-4 | 7.79 | | |
| | Omni-SC | ROUGE-L | 24.84 | | |
| | Omni-SC | CIDEr | 31.28 | | |
| | Omni-SC | METEOR | 13.20 | | |
| On the clean 512-video DVC evaluation using the historical one-round | |
| generation protocol, the model achieves SODA-C 5.057, METEOR 7.033, and | |
| CIDEr 12.156. | |
| For TVG, evaluation used constrained decoding to enforce LongVALE's official | |
| `From XX to YY` response syntax. Fair comparisons should use the same output | |
| constraint for every compared model; this checkpoint should not be interpreted | |
| as a claim about unconstrained free-form timestamp formatting. | |
| ## Limitations and use | |
| This is a research checkpoint, not a safety-validated production system. Its | |
| timestamps and captions can be inaccurate, especially for subtle audio events, | |
| very long videos, or videos outside the training distribution. The checkpoint | |
| does not include any LongVALE or AVTime media or annotations. Users must | |
| comply with the terms of the upstream base model and any input datasets. | |