Dynamic sampling is a technique employed in reinforcement Learning for Large Language Models to mitigate training instability, often caused by the gradient-decreasing problem. However, existing dynamic sampling approaches are predominantly designed for tasks with binary rewards (success/failure) and struggle to adapt to the complex reward structures of tool learning. Such task involves interdependent sub-tasks of varying difficulty and yields fine-grained, multi-faceted rewards. To bridge this gap, we introduce Dynamic Sampling with Curriculum Learning (DSCL), an algorithm tailored for the intricate dynamics of tool learning. DSCL integrates two core components: a Reward-Based Dynamic Sampler, which leverages multi-dimensional reward statistics to prioritize high-value training data; and a Task-Based Dynamic Curriculum Learning method to overcome the credit assignment problem caused by the fine-grained rewards. Extensive experiments on widely used tool learning benchmarks demonstrate the efficacy of our approach. DSCL significantly improves model performance, outperforming strong baselines by 4.75% on the BFCL V3 dataset and 4.02% on the API-Bank dataset.

In this paper we demonstrate our (OPPO’s) machine translation systems for the WMT20 Shared Task on News Translation for all the 22 language pairs. We will give an overview of the common aspects across all the systems firstly, including two parts: the data preprocessing part will show how the data are preprocessed and filtered, and the system part will show our models architecture and the techniques we followed. Detailed information, such as training hyperparameters and the results generated by each technique will be depicted in the corresponding subsections. Our final submissions ranked top in 6 directions (English ↔ Czech, English ↔ Russian, French → German and Tamil → English), third in 2 directions (English → German, English → Japanese), and fourth in 2 directions (English → Pashto and and English → Tamil).