3 citations · 5 across the 18 of their papers we have counts for
6 papers · 1 filter
From Word to World: Can Large Language Models be Implicit Text-based World Models?
Yixia Li, Hongru Wang, Jiahao Qiu +7
Agentic reinforcement learning increasingly relies on experience-driven scaling, yet real-world environments remain non-adaptive, limited in coverage, and difficult to scale. World…
G2: Guided Generation for Enhanced Output Diversity in LLMs
Zhiwen Ruan, Yixia Li, Yefeng Liu +5
Large Language Models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks. However, these models exhibit a critical limitation in outp…
Enhancing Large Language Model Reasoning via Selective Critical Token Fine-Tuning
Zhiwen Ruan, Yixia Li, He Zhu +4
Large language models (LLMs) primarily rely on supervised fine-tuning (SFT) as a key method to adapt pre-trained models to domain-specific tasks such as mathematical reasoning. How…
VisCodex: Unified Multimodal Code Generation via Merging Vision and Coding Models
Lingjie Jiang, Shaohan Huang, Xun Wu +3
Multimodal large language models (MLLMs) have significantly advanced the integration of visual and textual understanding. However, their ability to generate code from multimodal in…
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs
Yan Yang, Yixia Li, Hongru Wang +4
With the proliferation of task-specific large language models, delta compression has emerged as a method to mitigate the resource challenges of deploying numerous such models by ef…
LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy
Zhiwen Ruan, Yixia Li, He Zhu +5
Despite being pretrained on multilingual corpora, large language models (LLMs) exhibit suboptimal performance on low-resource languages. Recent approaches have leveraged multilingu…