5 citations · 5 across the 4 of their papers we have counts for
4 papers
LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Training
Bo Wu, Sid Wang, Yunhao Tang +11
Reinforcement Learning (RL) has become the most effective post-training approach for improving the capabilities of Large Language Models (LLMs). In practice, because of the high de…
Reinforcement Learning from User Feedback
Eric Han, Jun Chen, Karthik Abinav Sankararaman +8
As large language models (LLMs) are increasingly deployed in diverse user facing applications, aligning them with real user preferences becomes essential. Existing methods like Rei…
Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation
Chengwei Qin, Wenxuan Zhou, Karthik Abinav Sankararaman +10
Hallucination, the generation of factually incorrect information, remains a significant challenge for large language models (LLMs), especially in open-domain long-form generation.…
Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following
Yun He, Di Jin, Chaoqi Wang +16
Large Language Models (LLMs) have demonstrated impressive capabilities in various tasks, including instruction following, which is crucial for aligning model outputs with user expe…