Reinforcement Learning with Verifiable Rewards (RLVR) has markedly enhanced the reasoning abilities of large language models (LLMs).Its success, however, largely depends on strong base models with rich world knowledge, yielding only modest improvements for small-size language models (SLMs).To address this limitation, we investigate Guided GRPO, which injects ground-truth reasoning steps into roll-out trajectories to compensate for SLMs' inherent weaknesses.Through a comprehensive study of various guidance configurations, we find that naively adding guidance delivers limited gains.These insights motivate G 2 RPO-A, an adaptive algorithm that automatically adjusts guidance strength in response to the model's evolving training dynamics.Experiments on mathematical reasoning and codegeneration benchmarks confirm that G 2 RPO-A substantially outperforms vanilla GRPO.Our code and models are available at https:// github.com/T-Lab-CUHKSZ/G2RPO-A.

The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the efficiency of training and inference for Transformer-based foundational models, yielding promising results. However, the performance of SMoE heavily depends on the choice of hyper-parameters, such as the number of experts and the number of experts to be activated (referred to as top-$k$), resulting in significant computational overhead due to the extensive model training by searching over various hyper-parameter configurations. As a remedy, we introduce the Dynamic Mixture of Experts (DynMoE) technique. DynMoE incorporates (1) a novel gating method that enables each token to automatically determine the number of experts to activate. (2) An adaptive process automatically adjusts the number of experts during training. Extensive numerical results across Vision, Language, and Vision-Language tasks demonstrate the effectiveness of our approach to achieve competitive performance compared to GMoE for vision and language tasks, and MoE-LLaVA for vision-language tasks, while maintaining efficiency by activating fewer parameters. Our code is available at \url{https://github.com/LINs-lab/DynMoE}.

Wenqi Zhang, Zhenglin Cheng, Yuanyu He, Mengna Wang, Yongliang Shen, Zeqi Tan, Guiyang Hou, Mingqian He, Yanna Ma, Weiming Lu, Yueting Zhuang. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.