This is the Llama-3.2-3B-Instruct model trained by GRPO Ground Truth method using MATH training set. This model is one of the checkpoints released in conjunction with 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 training stability by seeking complementary supervision from multiple views, addressing the training collapse issue in single-view self-rewarding methods. The framework is instantiated in two ways: Co-rewarding-I (data-side, using contrastive agreement) and Co-rewarding-II (model-side, using a slowly-updated reference teacher). Intuitively, such instantiations introduce different levels of discrepancy to increase the difficulty of training collapse on trivial reasoning solutions.

For more details on the Co-rewarding framework, training procedures, and other related models and datasets, please refer to the official GitHub Repository.