11 citations · 19 across the 8 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.LG2026
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…
cs.LG2026★ 1 cited
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…
cs.CL2026
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…