1 citations · 1 across the 10 of their papers we have counts for
10 papers
Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception
Lai Wei, Liangbo He, Jun Lan +9
Multimodal Large Language Models (MLLMs) excel at broad visual understanding but still struggle with fine-grained perception, where decisive evidence is small and easily overwhelme…
Towards a Theoretical Understanding to the Generalization of RLHF
Zhaochun Li, Mingyang Yi, Yue Wang +2
Reinforcement Learning from Human Feedback (RLHF) and its variants have emerged as the dominant approaches for aligning Large Language Models with human intent. While empirically e…
ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment
Xiuyu Li, Jinkai Zhang, Mingyang Yi +4
Reinforcement Learning (RL) post-training alignment for language models is effective, but also costly and unstable in practice, owing to its complicated training process. To addres…
Stabilizing Policy Gradient Methods via Reward Profiling
Shihab Ahmed, El Houcine Bergou, Aritra Dutta +1
Policy gradient methods, which have been extensively studied in the last decade, offer an effective and efficient framework for reinforcement learning problems. However, their perf…
Pre-training Generative Recommender with Multi-Identifier Item Tokenization
Bowen Zheng, Enze Liu, Zhongfu Chen +4
Generative recommendation autoregressively generates item identifiers to recommend potential items. Existing methods typically adopt a one-to-one mapping strategy, where each item…
DGRO: Enhancing LLM Reasoning via Exploration-Exploitation Control and Reward Variance Management
Xuerui Su, Liya Guo, Yue Wang +4
Inference scaling further accelerates Large Language Models (LLMs) toward Artificial General Intelligence (AGI), with large-scale Reinforcement Learning (RL) to unleash long Chain-…
Symbolic Representation for Any-to-Any Generative Tasks
Jiaqi Chen, Xiaoye Zhu, Yue Wang +9
We propose a symbolic generative task description language and a corresponding inference engine capable of representing arbitrary multimodal tasks as structured symbolic flows. Unl…
Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning
Xuerui Su, Shufang Xie, Guoqing Liu +7
Recently, Large Language Models (LLMs) have rapidly evolved, approaching Artificial General Intelligence (AGI) while benefiting from large-scale reinforcement learning to enhance H…
Reveal the Mystery of DPO: The Connection between DPO and RL Algorithms
Xuerui Su, Yue Wang, Jinhua Zhu +4
With the rapid development of Large Language Models (LLMs), numerous Reinforcement Learning from Human Feedback (RLHF) algorithms have been introduced to improve model safety and a…
Disentangling Length Bias In Preference Learning Via Response-Conditioned Modeling
Jianfeng Cai, Jinhua Zhu, Ruopei Sun +4
Reinforcement Learning from Human Feedback (RLHF) has achieved considerable success in aligning large language models (LLMs) by modeling human preferences with a learnable reward m…