ModelVerificadoExterno

ReflectRL Qwen2.5 Math 7B GRPO ReflectRL

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

Fontehuggingface.co/Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL/tree/8ad563a01d331f5de99a4e5f3b51a195937d9f83

TipoText modelModelo baseQwen/Qwen2.5-Math-7BVersãomodelPublicador@JinheCnota

Fonte do modelo

Tipo: Text model
Tamanho do arquivo: 28,4 GB
Modelo base: Qwen/Qwen2.5-Math-7B
Versão: vmodel
Atualizado: 4 de out. de 2026
Publicador: @Jinhe
Downloads: 27
Parâmetros: 7B
Fonte: Hugging Face

Descrição da fonte

ReflectRL

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

Code & Repository: GitHub - ReflectRL
Base Model: Qwen/Qwen2.5-Math-7B
Paper: ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning

Overview

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={2027}
}

Fontes

1 fonte

Hugging FacePrimárioVersão 8ad563a01d331f5de99a4e5f3b51a195937d9f83

huggingface.co/Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL/tree/8ad563a01d331f5de99a4e5f3b51a195937d9f83

Verificado 4 de out.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 28,4 GB · SHA-256 c400634a3498…a47c · Hugging Face

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Trechos de fonte

3 trechos

Readme4 de out.huggingface.co/Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL

--- license: apache-2.0 library_name: transformers pipeline_tag: text-generation base_model: Qwen/Qwen2.5-Math-7B --- # ReflectRL This model checkpoint is part of **ReflectRL**, presented in the paper [ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning](https://huggingface.co/papers/2608.03972). - **Code & Repository:** [GitHub - ReflectRL](https://github.com/bibisbar/ReflectRL) - **Base Model:** [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) - **Paper:** [ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning](https://huggingface.co/papers/2608.03972) ## Overview 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 ```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={2027} } ```

Note4 de out.huggingface.co/Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL

Source context: 27 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL

Note18 de jul.huggingface.co/Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL

Source context: 189 downloads · 0 likes · Repo Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO-ReflectRL