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Tencent-Hunyuan/UniRL
UniRL is a Framework for Unified Multimodal Model Reinforcement Learning
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UniRL is a Framework for Unified Multimodal Model Reinforcement Learning
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Deep-Dive FAQs
What is Tencent-Hunyuan/UniRL?
Tencent-Hunyuan/UniRL is a digital product or tool described as: UniRL is a Framework for Unified Multimodal Model Reinforcement Learning
Where did Tencent-Hunyuan/UniRL originate?
Data for Tencent-Hunyuan/UniRL was aggregated directly from the GitHub Open Source community ecosystem, representing raw developer and early-adopter sentiment.
When was Tencent-Hunyuan/UniRL publicly launched?
The initial public indexing or launch date for Tencent-Hunyuan/UniRL within our tracked developer communities was recorded on June 8, 2026.
How popular is Tencent-Hunyuan/UniRL?
Tencent-Hunyuan/UniRL has achieved measurable traction, logging over 662 traction score and facilitating 38 recorded discussions or engagements.
Which technical categories define Tencent-Hunyuan/UniRL?
Based on metadata extraction, Tencent-Hunyuan/UniRL is categorized under topics such as: ai-infrastructure, reinforcement-learning, sglang, vllm.
Are there active development issues for Tencent-Hunyuan/UniRL?
Yes, we are currently tracking open architectural debates and bug reports for this project on GitHub. There are currently 4 active high-priority issues logged recently.
How does the creator describe Tencent-Hunyuan/UniRL?
The original author or development team describes the product as follows: "UniRL is a Framework for Unified Multimodal Model Reinforcement Learning"
Active Developer Issues (GitHub)
Logged: Jun 10, 2026
Logged: Jun 10, 2026
Logged: Jun 9, 2026
Logged: Jun 9, 2026
Community Voice & Feedback
@Maekami I think `Eculid/sd3.5-flowdppo` is owned by you. Would you please do that for the community?
Thanks for uploading the models to the Hugging Face hub. I can see that the models under Tencent-Hunyuan-Multimodal-RL are properly linked to the paper page.
However, the Eculid/sd3.5-flowdppo model (mentioned in the README) does not appear to be linked to the paper page yet. Could you add a README to that model repository that mentions the paper URL (https://huggingface.co/papers/2606.11025)? This will automatically link it to the paper page.
See the documentation here for more information: https://huggingface.co/docs/hub/model-cards#linking-a-paper
However, the Eculid/sd3.5-flowdppo model (mentioned in the README) does not appear to be linked to the paper page yet. Could you add a README to that model repository that mentions the paper URL (https://huggingface.co/papers/2606.11025)? This will automatically link it to the paper page.
See the documentation here for more information: https://huggingface.co/docs/hub/model-cards#linking-a-paper
> Thanks for your interest!
>
> Yes, the framework already supports the image understanding task (Image + Text → Text). We currently ship Qwen2.5-VL with full RL support — see the `unirl/models/qwen_vl/` pipeline and the example recipe `examples/ar/qwen_vl_grpo_geo3k_mc_4x8.yaml` (GRPO on Geometry3K, with SGLang and LoRA variants as well; we have tested the training and the reward curve increases well).
Hi， great work. Can this RL framework support think mode, like UniGRPO.
>
> Yes, the framework already supports the image understanding task (Image + Text → Text). We currently ship Qwen2.5-VL with full RL support — see the `unirl/models/qwen_vl/` pipeline and the example recipe `examples/ar/qwen_vl_grpo_geo3k_mc_4x8.yaml` (GRPO on Geometry3K, with SGLang and LoRA variants as well; we have tested the training and the reward curve increases well).
Hi， great work. Can this RL framework support think mode, like UniGRPO.
> Thanks for your interest!
>
> Yes, the framework already supports the image understanding task (Image + Text → Text). We currently ship Qwen2.5-VL with full RL support — see the `unirl/models/qwen_vl/` pipeline and the example recipe `examples/ar/qwen_vl_grpo_geo3k_mc_4x8.yaml` (GRPO on Geometry3K, with SGLang and LoRA variants as well; we have tested the training and the reward curve increases well).
Thanks a lot. Qwen2.5-VL is ok. How about the RL for image understanding task on Bagel?
>
> Yes, the framework already supports the image understanding task (Image + Text → Text). We currently ship Qwen2.5-VL with full RL support — see the `unirl/models/qwen_vl/` pipeline and the example recipe `examples/ar/qwen_vl_grpo_geo3k_mc_4x8.yaml` (GRPO on Geometry3K, with SGLang and LoRA variants as well; we have tested the training and the reward curve increases well).
Thanks a lot. Qwen2.5-VL is ok. How about the RL for image understanding task on Bagel?
Thanks for your interest!
Yes, the framework already supports the image understanding task (Image + Text → Text). We currently ship Qwen2.5-VL with full RL support — see the `unirl/models/qwen_vl/` pipeline and the example recipe `examples/ar/qwen_vl_grpo_geo3k_mc_4x8.yaml` (GRPO on Geometry3K, with SGLang and LoRA variants as well; we have tested the training and the reward curve increases well).
Yes, the framework already supports the image understanding task (Image + Text → Text). We currently ship Qwen2.5-VL with full RL support — see the `unirl/models/qwen_vl/` pipeline and the example recipe `examples/ar/qwen_vl_grpo_geo3k_mc_4x8.yaml` (GRPO on Geometry3K, with SGLang and LoRA variants as well; we have tested the training and the reward curve increases well).
Thanks. The ckpts have been updated with links to the paper.
Discovery Source
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