5 citations · 9 across the 13 of their papers we have counts for
5 papers · 1 filter
Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models
Tingchen Fu, Jiawei Gu, Yafu Li +2
Instruction-following is essential for aligning large language models (LLMs) with user intent. While recent reasoning-oriented models exhibit impressive performance on complex math…
CoCA: Step-level Reward for Free in RL-based T2I Diffusion Model Fine-tuning
Xinyao Liao, Wei Wei, Xiaoye Qu +3
Recent advances in text-to-image (T2I) diffusion model fine-tuning leverage reinforcement learning (RL) to align generated images with learnable reward functions. The existing appr…
SATORI-R1: Incentivizing Multimodal Reasoning through Explicit Visual Anchoring
Chuming Shen, Wei Wei, Xiaoye Qu +1
DeepSeek-R1 has demonstrated powerful reasoning capabilities in the text domain through stable reinforcement learning (RL). Recently, in the multimodal domain, works have begun to…
Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think
Jie Tian, Xiaoye Qu, Zhenyi Lu +3
Image-to-Video (I2V) generation aims to synthesize a video clip according to a given image and condition (e.g., text). The key challenge of this task lies in simultaneously generat…
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment
Chenghao Fan, Zhenyi Lu, Sichen Liu +4
While Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning for Large Language Models (LLMs), its performance often falls short of Full Fine-Tuning (Full FT). Current…