ReflectRL Qwen2.5 Math 7B GRPO
This repository contains model weights trained using ReflectRL, as presented in the paper ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning.
Source du modèle
- Type
- Text model
- Taille du fichier
- 28,4 GB
- Modèle de base
- Qwen2.5
- Version
- vmodel
- Mis à jour
- 4 oct. 2026
- Éditeur
- @Jinhe
- Téléchargements
- 23
- Paramètres
- 7B
- Source
- Hugging Face
Description de la source
ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning
This repository contains model weights trained using ReflectRL, as presented in the paper ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning.
- Paper: ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning
- Repository: GitHub - bibisbar/ReflectRL
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}
}
Sources
1 source
Sources
1 sourceVérifié 4 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- license: apache-2.0 library_name: transformers pipeline_tag: text-generation --- # ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning This repository contains model weights trained using **ReflectRL**, as presented in the paper [ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning](https://huggingface.co/papers/2608.03972). - **Paper:** [ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning](https://huggingface.co/papers/2608.03972) - **Repository:** [GitHub - bibisbar/ReflectRL](https://github.com/bibisbar/ReflectRL) ## 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} } ```
Source context: 23 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO
Source context: 191 downloads · 0 likes · Repo Jinhe/ReflectRL-Qwen2.5-Math-7B-GRPO
