Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Yichen Dong, Xinwei Wu, Jiang Zhou, Tianyu Dong, Xiangxiang Zeng, Longyue Wang, Weihua Luo. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Distributed training (DT) has emerged as a solution to address the growing computational resource demands of training large-scale machine learning models. To meet this need, cloud providers typically build GPU clusters to accommodate DT jobs. For DT job requests, cloud providers need to determine in which GPUs place workers (i.e., job placement). Existing approaches usually place workers on as few idle machines as possible to minimize communication time. However, this scheme will lead to aresource fragmentation problem, which degrades the resource utilization rate of the GPU cluster and increases training costs for cloud providers. In this paper, we propose$\textsf {Titan}$, a novel job placement scheme that mitigates the influence of resource fragmentation by enhancing the utilization of non-idle machines. To further optimize resource allocation, we introduce a dynamic defragmentation algorithm that migrates fragmented jobs to consolidate GPU resources, enabling efficient placement of large-scale training jobs.$\textsf {Titan}$formulates a multi-objective non-linear optimization problem and proves its NP-hardness. To solve this problem,$\textsf {Titan}$presents an effective submodular-based greedy algorithm with a tight approximation ratio ($1-\frac {1}{e}$). We evaluate$\textsf {Titan}$with a large-scale simulation employing real-world job traces and a small-scale testbed consisting of 8 servers with 32 logical GPUs. Experimental results show that$\textsf {Titan}$can achieve near-optimal training throughput while improving the efficiency of the cluster by 74.9% compared to the state-of-the-art solutions.

Recent research has shown that large language models (LLMs) can enhance translation quality through self-refinement. In this paper, we build on this idea by extending the refinement from sentence-level to document-level translation, specifically focusing on document-to-document (Doc2Doc) translation refinement. Since sentence-to-sentence (Sent2Sent) and Doc2Doc translation address different aspects of the translation process, we propose fine-tuning LLMs for translation refinement using two intermediate translations, combining the strengths of both Sent2Sent and Doc2Doc. Additionally, recognizing that the quality of intermediate translations varies, we introduce an enhanced fine-tuning method with quality awareness that assigns lower weights to easier translations and higher weights to more difficult ones, enabling the model to focus on challenging translation cases. Experimental results across ten translation tasks with LLaMA-3-8B-Instruct and Mistral-Nemo-Instruct demonstrate the effectiveness of our approach. We will release our code on GitHub.