1 citations · 1 across the 3 of their papers we have counts for
7 papers
Reinforcement Fine-Tuning for History-Aware Dense Retriever in RAG
Yicheng Zhang, Zhen Qin, Zhaomin Wu +2
Retrieval-augmented generation (RAG) enables large language models (LLMs) to produce evidence-based responses, and its performance hinges on the matching between the retriever and…
AlphaGo Moment for Model Architecture Discovery
Yixiu Liu, Yang Nan, Weixian Xu +4
While AI systems demonstrate exponentially improving capabilities, the pace of AI research itself remains linearly bounded by human cognitive capacity, creating an increasingly sev…
Accelerate TarFlow Sampling with GS-Jacobi Iteration
Ben Liu, Zhen Qin
Image generation models have achieved widespread applications. As an instance, the TarFlow model combines the transformer architecture with Normalizing Flow models, achieving state…
Hybrid Latent Reasoning via Reinforcement Learning
Zhenrui Yue, Bowen Jin, Huimin Zeng +6
Recent advances in large language models (LLMs) have introduced latent reasoning as a promising alternative to autoregressive reasoning. By performing internal computation with hid…
Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?
Hansi Zeng, Kai Hui, Honglei Zhuang +4
While metrics available during pre-training, such as perplexity, correlate well with model performance at scaling-laws studies, their predictive capacities at a fixed model size re…
Rankers, Judges, and Assistants: Towards Understanding the Interplay of LLMs in Information Retrieval Evaluation
Krisztian Balog, Donald Metzler, Zhen Qin
Large language models (LLMs) are increasingly integral to information retrieval (IR), powering ranking, evaluation, and AI-assisted content creation. This widespread adoption neces…