1 citations · 1 across the 9 of their papers we have counts for
10 papers
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis
Qiushi Sun, Jingyang Gong, Yang Liu +14
Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is burgeoning, as models are increas…
Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation
Hanxu Hu, Zdeněk Šnajdr, Pinzhen Chen +2
Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encoding a grammar book in their c…
DeReason: A Difficulty-Aware Curriculum Improves Decoupled SFT-then-RL Training for General Reasoning
Hanxu Hu, Yuxuan Wang, Maggie Huan +4
Reinforcement learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for eliciting reasoning capabilities in large language models, particularly in mathematics…
CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
Xiao Zhu, Xinyu Zhou, Boyu Zhu +5
Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based feedback from unit tests, but its scala…
QueST: Incentivizing LLMs to Generate Difficult Problems
Hanxu Hu, Xingxing Zhang, Jannis Vamvas +2
Large Language Models have achieved strong performance on reasoning tasks, solving competition-level coding and math problems. However, their scalability is limited by human-labele…
CHARM: Calibrating Reward Models With Chatbot Arena Scores
Xiao Zhu, Chenmien Tan, Pinzhen Chen +4
Reward models (RMs) play a crucial role in Reinforcement Learning from Human Feedback by serving as proxies for human preferences in aligning large language models. However, they s…
Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents
Hanxu Hu, Jannis Vamvas, Rico Sennrich
LLMs have paved the way for truly simple document-level machine translation, but challenges such as omission errors remain. In this paper, we study a simple method for handling doc…
Generalizing From Short to Long: Effective Data Synthesis for Long-Context Instruction Tuning
Wenhao Zhu, Pinzhen Chen, Hanxu Hu +4
Long-context modelling for large language models (LLMs) has been a key area of recent research because many real world use cases require reasoning over longer inputs such as docume…
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models
Xu Huang, Wenhao Zhu, Hanxu Hu +4
Previous multilingual benchmarks focus primarily on simple understanding tasks, but for large language models(LLMs), we emphasize proficiency in instruction following, reasoning, l…
Fine-tuning Large Language Models with Sequential Instructions
Hanxu Hu, Simon Yu, Pinzhen Chen +1
Despite the success of existing instruction-tuned models, we find that they usually struggle to respond to queries with multiple instructions. This impairs their performance in com…