From the 1 of 36 linked papers with an AI index.
2 citations · 2 across the 11 of their papers we have counts for
17 papers · 1 filter
Self-Improving Large Language Models via Progressive Experience Evolution
Shijie Ren, Xiting Wang, Meng Li +8
Large language models (LLMs) capable of self-improvement require not only effective policy optimization, but also a principled mechanism for transforming transient interaction expe…
LEEPS: Latent-Guided Explore-Exploit Prompt Sampling for Efficient RLVR in Large Language Models
Shuang Liang, Haoyang Zhou, Yifan Gong +2
The paper introduces LEEPS, a latent-guided explore‑exploit prompt sampler that selects prompts before rollout to reduce wasted generation budget and improve reinforcement learning…
Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG
Haotian Zhou, Weiran Huang, Siqi Liu +3
Cross-lingual retrieval-augmented generation (RAG) is often deployed in an English-evidence regime, where users query in diverse languages but retrieved passages remain English. In…
TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM
Haoyang Zhou, Li Kong, Shijie Ren +4
Diffusion large language models (dLLMs) offer a promising paradigm for parallel text generation, but in practice they face an accuracy-parallelism trade-off, where increasing token…
Enhancing Safety of Large Language Models via Embedding Space Separation
Xu Zhao, Xiting Wang, Weiran Shen
Large language models (LLMs) have achieved impressive capabilities, yet ensuring their safety against harmful prompts remains a critical challenge. Recent work has revealed that th…
Evaluating Text Creativity across Diverse Domains: A Dataset and Large Language Model Evaluator
Qian Cao, Xiting Wang, Yuzhuo Yuan +3
Creativity evaluation remains a challenging frontier for large language models (LLMs). Current evaluations heavily rely on inefficient and costly human judgments, hindering progres…