11 citations · 19 across the 8 of their papers we have counts for
11 papers
Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misalignment
Hong Li, Zhen Zhou, Honggang Zhang +4
Data-parallel (DP) training with synchronous all-reduce is a dominant paradigm for full-parameter fine-tuning of large language models (LLMs). While parameter synchronization guara…
Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
Xiaodong Lu, Xiaohan Wang, Jiajun Chai +7
Reinforcement Learning with Verifiable Rewards (RLVR) is an effective paradigm for improving the reasoning capabilities of large language models. However, existing RLVR methods uti…
ChiEngMixBench: Evaluating Large Language Models on Expert-Style Chinese-English Terminology Mixing
Qingyan Yang, Tongxi Wang, Yunsheng Luo
Large language models increasingly mediate multilingual professional communication, where useful generation requires adapting to community conventions about which expressions are r…
Towards Robust Zero-Shot Reinforcement Learning
Kexin Zheng, Lauriane Teyssier, Yinan Zheng +2
The recent development of zero-shot reinforcement learning (RL) has opened a new avenue for learning pre-trained generalist policies that can adapt to arbitrary new tasks in a zero…
Towards Real-Time Fake News Detection under Evidence Scarcity
Guangyu Wei, Ke Han, Yueming Lyu +4
Fake news detection becomes particularly challenging in real-time scenarios, where emerging events often lack sufficient supporting evidence. Existing approaches often rely heavily…
Omni-Thinker: Scaling Multi-Task RL in LLMs with Hybrid Reward and Task Scheduling
Derek Li, Jiaming Zhou, Leo Maxime Brunswic +8
The pursuit of general-purpose artificial intelligence depends on large language models (LLMs) that can handle both structured reasoning and open-ended generation. We present Omni-…
MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings
Haonan Chen, Hong Liu, Yuping Luo +4
Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face three key limitations: the use o…
Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs
Jack Chen, Fazhong Liu, Naruto Liu +7
Large language models (LLMs) excel at mathematical reasoning and logical problem-solving. The current popular training paradigms primarily use supervised fine-tuning (SFT) and rein…
Simulation as Reality? The Effectiveness of LLM-Generated Data in Open-ended Question Assessment
Long Zhang, Meng Zhang, Wei Lin Wang +1
The advancement of Artificial Intelligence (AI) has created opportunities for e-learning, particularly in automated assessment systems that reduce educators' workload and provide t…
Harnessing Multiple Large Language Models: A Survey on LLM Ensemble
Zhijun Chen, Xiaodong Lu, Jingzheng Li +12
LLM Ensemble -- which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from thei…
Efficient LLM Inference on CPUs
Haihao Shen, Hanwen Chang, Bo Dong +2
Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. However, deploying these models has been challenging du…