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ReflectRL Qwen2.5 Math 7B DAPO

This model checkpoint is part of the work presented in ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning.

Sourcehuggingface.co/Jinhe/ReflectRL-Qwen2.5-Math-7B-DAPO/tree/bb6557fc35419474bcf887ab5e2c81abc8ecf38a
TypeText modelModèle de baseQwen/Qwen2.5-Math-7BVersionmodelÉditeur@JinheCClasse

Source du modèle

Type
Text model
Taille du fichier
28,4 GB
Modèle de base
Qwen/Qwen2.5-Math-7B
Version
vmodel
Mis à jour
4 oct. 2026
Éditeur
@Jinhe
Téléchargements
17
Paramètres
7B
Source
Hugging Face

Description de la source

ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL

This model checkpoint is part of the work presented in ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning.

ReflectRL is a lightweight framework for learning from Golden Negative Trajectories (GNTs) during on-policy post-training. Instead of imitating failed expert trajectories directly, ReflectRL uses them as reflective context during training and gradually transitions the policy back to direct reasoning for inference.

Citation

@article{reflectrl2027,
  title={ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning},
  author={Jinhe Bi and Chennan Zhou and Zengjie Jin and Aniri and Shuo Lu and Wenke Huang and Hu Cao and Xun Xiao and Zhihong Zhu and Volker Tresp and Fei Shen and Yunpu Ma and Tat-Seng Chua},
  year={2026}
}

Sources

1 source
Hugging FacePrincipalVersion bb6557fc35419474bcf887ab5e2c81abc8ecf38a
huggingface.co/Jinhe/ReflectRL-Qwen2.5-Math-7B-DAPO/tree/bb6557fc35419474bcf887ab5e2c81abc8ecf38a

Vérifié 4 oct.

Artefacts du modèle

1 artefact

model.safetensors

safetensors · 28,4 GB · SHA-256 926f8e2066dd…1ea5 · Hugging Face

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Extraits de sources

3 extraits
Readme4 oct.huggingface.co/Jinhe/ReflectRL-Qwen2.5-Math-7B-DAPO

--- license: apache-2.0 library_name: transformers pipeline_tag: text-generation base_model: Qwen/Qwen2.5-Math-7B --- # ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL This model checkpoint is part of the work presented in [ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning](https://huggingface.co/papers/2608.03972). ReflectRL is a lightweight framework for learning from Golden Negative Trajectories (GNTs) during on-policy post-training. Instead of imitating failed expert trajectories directly, ReflectRL uses them as reflective context during training and gradually transitions the policy back to direct reasoning for inference. - **Paper:** [ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning](https://huggingface.co/papers/2608.03972) - **Repository:** [GitHub Repository](https://github.com/bibisbar/ReflectRL) ## Citation ```bibtex @article{reflectrl2027, title={ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning}, author={Jinhe Bi and Chennan Zhou and Zengjie Jin and Aniri and Shuo Lu and Wenke Huang and Hu Cao and Xun Xiao and Zhihong Zhu and Volker Tresp and Fei Shen and Yunpu Ma and Tat-Seng Chua}, year={2026} } ```

Note4 oct.huggingface.co/Jinhe/ReflectRL-Qwen2.5-Math-7B-DAPO

Source context: 17 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo Jinhe/ReflectRL-Qwen2.5-Math-7B-DAPO

Note18 juil.huggingface.co/Jinhe/ReflectRL-Qwen2.5-Math-7B-DAPO

Source context: 188 downloads · 0 likes · Repo Jinhe/ReflectRL-Qwen2.5-Math-7B-DAPO