8 papers
TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation
Huichao Chai, Zhixin Wu, Xuemiao Li +8
Generative recommendation (GR) has emerged as a promising paradigm that replaces fragmented, scenario-specific architectures with unified Transformer-based models, exhibiting scali…
GRRM: Group Relative Reward Modeling for Machine Translation
Sen Yang, Shanbo Cheng, Lu Xu +2
While Group Relative Policy Optimization (GRPO) offers a powerful framework for LLM post-training, its effectiveness in open-ended domains like Machine Translation hinges on accura…
CrowdAgent: Multi-Agent Managed Multi-Source Annotation System
Maosheng Qin, Renyu Zhu, Mingxuan Xia +8
High-quality annotated data is a cornerstone of modern Natural Language Processing (NLP). While recent methods begin to leverage diverse annotation sources-including Large Language…
AetherCode: Evaluating LLMs' Ability to Win In Premier Programming Competitions
Zihan Wang, Jiaze Chen, Zhicheng Liu +25
Competitive programming has emerged as a critical benchmark for evaluating the reasoning and coding capabilities of Large Language Models (LLMs). Despite impressive progress on exi…
Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters
Shanbo Cheng, Yu Bao, Qian Cao +23
Multilingual translation stands as a challenging task for large language models (LLMs) to handle intricate language patterns and stilted translations that arise in automated transl…
DuPO: Enabling Reliable LLM Self-Verification via Dual Preference Optimization
Shuaijie She, Yu Bao, Yu Lu +7
We present DuPO, a dual learning-based preference optimization framework that generates annotation-free feedback via a generalized duality. DuPO addresses two key limitations: Rein…