29 citations · 33 across the 14 of their papers we have counts for
6 papers · 1 filter
Explore Data Left Behind in Reinforcement Learning for Reasoning Language Models
Chenxi Liu, Junjie Liang, Yuqi Jia +4
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for improving the reasoning abilities of large language models (LLMs). The Group Relative…
Why Does Weak-OOD Help? A Further Step Towards Understanding Jailbreaking VLMs
Yuxuan Zhou, Yuzhao Peng, Yang Bai +7
Large Vision-Language Models (VLMs) are susceptible to jailbreak attacks: researchers have developed various attack strategies that bypass the safety mechanisms of VLMs. Among thes…
AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy
Jinghang Shi, Xiaoyu Tang, Yang Huang +4
Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…
OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling
Jihu Guo, Tenghui Ma, Wei Gao +6
Pipeline parallelism is widely used to train large language models (LLMs). However, increasing heterogeneity in model architectures exacerbates pipeline bubbles, thereby reducing t…
Blackbox Dataset Inference for LLM
Ruikai Zhou, Kang Yang, Xun Chen +3
Today, the training of large language models (LLMs) can involve personally identifiable information and copyrighted material, incurring dataset misuse. To mitigate the problem of d…
Alleviating the Fear of Losing Alignment in LLM Fine-tuning
Kang Yang, Guanhong Tao, Xun Chen +1
Large language models (LLMs) have demonstrated revolutionary capabilities in understanding complex contexts and performing a wide range of tasks. However, LLMs can also answer ques…