5 papers
Leveraging LLM-based agents for social science research: insights from citation network simulations
Jiarui Ji, Runlin Lei, Xuchen Pan +8
The emergence of Large Language Models (LLMs) demonstrates their potential to encapsulate the logic and patterns inherent in human behavior simulation by leveraging extensive web d…
Grounded in Reality: Learning and Deploying Proactive LLM from Offline Logs
Fei Wei, Daoyuan Chen, Ce Wang +5
Large Language Models (LLMs) excel as passive responders, but teaching them to be proactive, goal-oriented partners, a critical capability in high-stakes domains, remains a major c…
Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models
Xuchen Pan, Yanxi Chen, Yushuo Chen +11
Trinity-RFT is a general-purpose, unified and easy-to-use framework designed for reinforcement fine-tuning (RFT) of large language models. It is built with a modular and decoupled…
Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models
Daoyuan Chen, Yilun Huang, Xuchen Pan +12
Foundation models demand advanced data processing for their vast, multimodal datasets. However, traditional frameworks struggle with the unique complexities of multimodal data. In…
Provable Scaling Laws for the Test-Time Compute of Large Language Models
Yanxi Chen, Xuchen Pan, Yaliang Li +2
We propose two simple, principled and practical algorithms that enjoy provable scaling laws for the test-time compute of large language models (LLMs). The first one is a two-stage…