6 papers
ViSS-R1: Self-Supervised Reinforcement Video Reasoning
Bo Fang, Yuxin Song, Qiangqiang Wu +3
Complex video reasoning remains a significant challenge for Multimodal Large Language Models (MLLMs), as current R1-based methodologies often prioritize text-centric reasoning deri…
Why mask diffusion does not work
Haocheng Sun, Cynthia Xin Wen, Edward Hong Wang
The main advantages of diffusion language models over autoregressive (AR) models lie in their ability to support parallel generation and bidirectional attention, enabling a more co…
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
Hao Sun, Mihaela van der Schaar
In the era of Large Language Models (LLMs), alignment has emerged as a fundamental yet challenging problem in the pursuit of more reliable, controllable, and capable machine intell…
Rethinking Diverse Human Preference Learning through Principal Component Analysis
Feng Luo, Rui Yang, Hao Sun +5
Understanding human preferences is crucial for improving foundation models and building personalized AI systems. However, preferences are inherently diverse and complex, making it…
Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes
Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun +1
As large language models (LLMs) become increasingly embedded in everyday applications, ensuring their alignment with the diverse preferences of individual users has become a critic…
The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data
Thomas Pouplin, Katarzyna Kobalczyk, Hao Sun +1
Developing autonomous agents capable of performing complex, multi-step decision-making tasks specified in natural language remains a significant challenge, particularly in realisti…