6 papers
A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning
Mengqi Li, Lei Zhao, Anthony Man-Cho So +2
Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training? We show that they can. We propose Self-evolvi…
Disentangling Language Roles in Multilingual LLM Task Execution
Qishi Zhan, Minxuan Hu, Seoyeon Jang +7
Multilingual LLMs are increasingly used when instruction, source content, and required response languages do not coincide. Existing benchmarks have expanded multilingual instructio…
TikArt: Stabilizing Aperture-Guided Fine-Grained Visual Reasoning with Reinforcement Learning
Hao Ding, Zhichuan Yang, Weijie Ge +3
Fine-grained visual reasoning in multimodal large language models (MLLMs) is bottlenecked by single-pass global image encoding: key evidence often lies in tiny objects, cluttered r…
LGM: Enhancing Large Language Models with Conceptual Meta-Relations and Iterative Retrieval
Wenchang Lei, Ping Zou, Yue Wang +2
Large language models (LLMs) exhibit strong semantic understanding, yet struggle when user instructions involve ambiguous or conceptually misaligned terms. We propose the Language…
DPO-Shift: Shifting the Distribution of Direct Preference Optimization
Xiliang Yang, Feng Jiang, Qianen Zhang +2
Direct Preference Optimization (DPO) and its variants have become increasingly popular for aligning language models with human preferences. These methods aim to teach models to bet…
StreamBP: Memory-Efficient Exact Backpropagation for Long Sequence Training of LLMs
Qijun Luo, Mengqi Li, Lei Zhao +1
Training language models on long sequence data is a demanding requirement for enhancing the model's capability on complex tasks, e.g., long-chain reasoning. However, as the sequenc…