11 papers
Reinforcement Learning without Ground-Truth Solutions can Improve LLMs
Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang +6
Reinforcement learning with verifiable rewards (RLVR) for training LLMs typically rely on ground-truth answers to assign rewards, limiting their applicability to tasks where the gr…
Training a Large Language Model for Medical Coding Using Privacy-Preserving Synthetic Clinical Data
John Cook, Michael Wyatt, Peng Wei +11
Improving the accuracy and reliability of medical coding reduces clinician burnout and supports revenue cycle processes, freeing providers to focus more on patient care. However, a…
DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science
Fan Shu, Yite Wang, Ruofan Wu +4
The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need for accurate benchmarking. There are two majo…
Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences
Stas Bekman, Samyam Rajbhandari, Michael Wyatt +5
Long sequences are critical for applications like RAG, long document summarization, multi-modality, etc., and modern LLMs, like Llama 4 Scout, support max sequence length of up to…
MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility
Yexiao He, Ang Li, Boyi Liu +2
Healthcare decision-making represents one of the most challenging domains for Artificial Intelligence (AI), requiring the integration of diverse knowledge sources, complex reasonin…
ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering
Ruofan Wu, Youngwon Lee, Fan Shu +5
Retrieval-Augmented Generation (RAG) systems are increasingly diverse, yet many suffer from monolithic designs that tightly couple core functions like query reformulation, retrieva…