From the 1 of 5 linked papers with an AI index.
5 papers
Weak-to-Strong On-Policy Distillation
Fangxu Yu, Zinan Lin, Xiaodong Liu +4
The paper proposes Weak-to-Strong On-Policy Distillation (W2S-OPD), a method that improves a large language model by distilling knowledge from multiple weaker models using a constr…
DistFlow: A Fully Distributed RL Framework for Scalable and Efficient LLM Post-Training
Zhixin Wang, Jiaming Xu, Tianyi Zhou +10
Effectively scaling Reinforcement Learning (RL) is crucial for enhancing the reasoning and alignment of Large Language Models. The massive data and complex execution flows inherent…
Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Models
Tianyi Zhou, Johanne Medina, Sanjay Chawla
Large Language Models (LLMs) are prone to generating fluent but incorrect content, known as confabulation, which poses increasing risks in multi-turn or agentic applications where…
Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play
Qinsi Wang, Bo Liu, Tianyi Zhou +6
Although reinforcement learning (RL) has emerged as a promising approach for improving vision-language models (VLMs) and multimodal large language models (MLLMs), current methods r…
Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning
Kwesi Cobbina, Tianyi Zhou
In-context learning (ICL) is a critical emerging capability of large language models (LLMs), enabling few-shot learning during inference by including a few demonstrations (demos) i…