1 citations · 2 across the 2 of their papers we have counts for
6 papers
Towards Robust Zero-Shot Reinforcement Learning
Kexin Zheng, Lauriane Teyssier, Yinan Zheng +2
The recent development of zero-shot reinforcement learning (RL) has opened a new avenue for learning pre-trained generalist policies that can adapt to arbitrary new tasks in a zero…
Towards Real-Time Fake News Detection under Evidence Scarcity
Guangyu Wei, Ke Han, Yueming Lyu +4
Fake news detection becomes particularly challenging in real-time scenarios, where emerging events often lack sufficient supporting evidence. Existing approaches often rely heavily…
Omni-Thinker: Scaling Multi-Task RL in LLMs with Hybrid Reward and Task Scheduling
Derek Li, Jiaming Zhou, Leo Maxime Brunswic +8
The pursuit of general-purpose artificial intelligence depends on large language models (LLMs) that can handle both structured reasoning and open-ended generation. We present Omni-…
MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings
Haonan Chen, Hong Liu, Yuping Luo +4
Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face three key limitations: the use o…
Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs
Jack Chen, Fazhong Liu, Naruto Liu +7
Large language models (LLMs) excel at mathematical reasoning and logical problem-solving. The current popular training paradigms primarily use supervised fine-tuning (SFT) and rein…
Simulation as Reality? The Effectiveness of LLM-Generated Data in Open-ended Question Assessment
Long Zhang, Meng Zhang, Wei Lin Wang +1
The advancement of Artificial Intelligence (AI) has created opportunities for e-learning, particularly in automated assessment systems that reduce educators' workload and provide t…