This model is the Qwen3-8B-Base, trained by Co-rewarding-I using the DAPO-14k training set. It was presented in the paper Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models.
Co-rewarding is a novel self-supervised reinforcement learning (RL) framework designed to improve the training stability of large language models (LLMs) for reasoning tasks. This particular model utilizes Co-rewarding-I, a data-side instantiation that derives reward signals from contrastive agreement across semantically analogous questions. This approach aims to mitigate the training collapse and reward hacking issues often encountered in single-view self-rewarding methods, thereby enhancing the LLM's reasoning abilities for complex challenges like mathematical reasoning.
For more details on the Co-rewarding framework, access to the code, and other trained checkpoints, please refer to the official GitHub repository: https://github.com/tmlr-group/Co-rewarding.
