While large-scale training data is fundamental for developing capable large language models (LLMs), strategically selecting high-quality data has emerged as a critical approach to enhance training efficiency and reduce computational costs. Current data selection methodologies predominantly rely on static, training-agnostic criteria, failing to account for the dynamic model training and data interactions. In this paper, we propose a new Data Weighting Model (DWM) to adjust the weight of selected data within each batch to achieve a dynamic data utilization during LLM training. Specially, to better capture the dynamic data preference of the trained model, a bi-level optimization framework is implemented to update the weighting model. Our experiments demonstrate that DWM enhances the performance of models trained with randomly-selected data, and the learned weighting model can be transferred to enhance other data selection methods and models of different sizes. Moreover, we further analyze how a model’s data preferences evolve throughout training, providing new insights into the data preference of the model during training.

Collaboration among multiple tasks is advantageous for enhancing learning efficiency in multiagent reinforcement learning. To guide agents in cooperating with different teammates in multiple tasks, contemporary approaches encourage agents to exploit common cooperative patterns or identify the learning priorities of multiple tasks. Despite the progress made by these methods, they all assume that all cooperative tasks to be learned are related and desire similar agent policies. This is rarely the case in multiagent cooperation, where minor changes in team composition can lead to significant variations in cooperation, resulting in distinct cooperative strategies compete for limited learning resources. In this article, to tackle the challenge posed by multitask learning in potentially competing cooperative tasks, we propose a novel framework called relation-aware learning (RAL). RAL incorporates a relation awareness module in both task representation and task optimization, aiding in reasoning about task relationships and mitigating negative transfers among dissimilar tasks. To assess the performance of RAL, we conduct a comparative analysis with baseline methods in a multitaskStarCraftenvironment. The results demonstrate the superiority of RAL in multitask cooperative scenarios, particularly in scenarios involving multiple conflicting tasks.

Learning cooperation in sparse-reward multi-agent reinforcement learning is challenging, since agents need to explore in the large joint-state space with sparse feedback. However, in cooperative games, the cooperative target is often related to partial attributes, hence there is no need to treat the whole state space equally. Therefore, we propose Underexplored Subspace Mining (USM), a novel type of intrinsic reward that encourages agents to selectively explore partial attributes instead of wasting time on the whole state space to accelerate learning. Specially, considering that the target-related attributes are varying in different games and hard to predefine, we choose to focus on the underexplored subspace as an alternative, which is an automatic aggregation of the underexplored bottom-level dimensions without any human design or learning parameters. We evaluate our method in cooperative games with discrete and continuous state space separately. Results demonstrate that USM consistently outperforms existing state-of-the-art methods, and becomes the only method that has succeeded in sparse-reward games evaluated with larger state space or more complicated cooperation dynamics.