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Minki Kang, Jongwon Jeong, Seanie Lee +2 authors
Agent Distillation transfers reasoning and task-solving capabilities from large language models to smaller models using enhanced prompts and self-consistent actions, matching performance of larger models on various reasoning tasks.
Xinjie Zhang, Jintao Guo, Shanshan Zhao +7 authors
A survey examines the integration of multimodal understanding and image generation models, categorizing them into diffusion-based, autoregressive-based, and hybrid approaches, and discussing challenges and future research directions.
Xiusi Chen, Gaotang Li, Ziqi Wang +9 authors
Reasoning Reward Models (ReasRMs) enhance reward modeling for large language models by integrating reasoning tasks, improving interpretability and performance.
Roberto L. Castro, Andrei Panferov, Soroush Tabesh +5 authors
Quartet, a hardware-supported FP4 training approach for large language models, demonstrates state-of-the-art accuracy while significantly reducing computational costs compared to standard or FP8 precision.
Mengzhao Chen, Chaoyi Zhang, Jing Liu +8 authors
A unified scaling law for quantization-aware training (QAT) identifies key factors affecting quantization error, leading to improvements through mixed-precision quantization.
Chenggang Zhao, Chengqi Deng, Chong Ruan +12 authors
DeepSeek-V3 addresses hardware limitations through MLA, MoE, FP8 training, and Multi-Plane Network Topology, enabling efficient large-scale LLM training and inference.
Jintao Zhang, Jia Wei, Pengle Zhang +6 authors
Efficiency enhancements for attention mechanisms, including leveraging FP4 Tensor Cores and developing an 8-bit attention method, improve inference and training performance.
Yumin Choi, Jinheon Baek, Sung Ju Hwang
A meta-learning framework for optimizing system prompts in Large Language Models (LLMs) improves generalization across diverse tasks and datasets.
Tianyu Fu, Yi Ge, Yichen You +6 authors
Roads to Rome (R2R) selectively utilizes large language models for critical reasoning tasks to enhance efficiency and performance in lightweight models.
Junteng Liu, Yuanxiang Fan, Zhuo Jiang +12 authors
SynLogic, a data synthesis framework, enhances the logical reasoning capabilities of Large Language Models through RL, achieving state-of-the-art performance and improving generalization across various domains.
Diankun Wu, Fangfu Liu, Yi-Hsin Hung +1 authors
Spatial-MLLM improves spatial reasoning in multimodal large language models using a dual-encoder architecture with pretrained 2D and 3D structure encoders, achieving state-of-the-art performance on visual spatial tasks.
Hao Sun, Zile Qiao, Jiayan Guo +6 authors
ZeroSearch is a reinforcement learning framework that improves LLM search capabilities without using real search engines, using supervised fine-tuning and curriculum-based rollouts to handle document quality and API cost challenges.
Guilong Lu, Xuntao Guo, Rongjunchen Zhang +2 authors
BizFinBench is a benchmark for evaluating large language models in financial applications, revealing distinct performance patterns across various tasks.
Doohyuk Jang, Yoonjeon Kim, Chanjae Park +2 authors
A diagnostic set examines and categorizes reasoning rigidity in large language models, identifying patterns where models ignore instructions and default to familiar reasoning.
Yaorui Shi, Sihang Li, Chang Wu +5 authors
AutoRefine, a reinforcement learning framework for large language models, enhances retrieval-augmented reasoning by iteratively refining knowledge and optimizing searches, leading to improved performance in complex question-answering tasks.
Yiren Song, Cheng Liu, Mike Zheng Shou
OmniConsistency, using large-scale Diffusion Transformers, enhances stylization consistency and generalization in image-to-image pipelines without style degradation.
Yan Ma, Linge Du, Xuyang Shen +7 authors
A unified reinforcement learning system, V-Triune, combines visual reasoning and perception tasks in vision-language models through a single training pipeline, achieving significant improvements across various tasks.
Tingchen Fu, Jiawei Gu, Yafu Li +2 authors
An empirical analysis of MathIF identifies a tension between enhancing reasoning capacity and maintaining instruction adherence in large language models.
Gleb Mezentsev, Ivan Oseledets
LLMs can generate long text segments in a single forward pass using learned embeddings, revealing a capability for multi-token generation without iterative decoding.
Yue Liu, Shengfang Zhai, Mingzhe Du +9 authors
GuardReasoner-VL enhances VLM safety through a reasoning-based guard model trained with SFT and online RL, using diverse datasets and a length-aware safety reward.
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