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Wei Xiong, Hanning Zhang, Chenlu Ye +3 authors
Self-rewarding reasoning large language models independently generate and correct their outputs during inference using a two-stage algorithmic framework, enhancing performance without external feedback.
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.
Xiaomi LLM-Core Team, Zihao Yue, Zhenru Lin +71 authors
MiMo-VL-7B-SFT and MiMo-VL-7B-RL provide state-of-the-art general visual understanding and multimodal reasoning through four-stage pre-training and Mixed On-policy Reinforcement Learning, outperforming models with up to 78B parameters.
Haozhan Li, Yuxin Zuo, Jiale Yu +18 authors
SimpleVLA-RL, an RL framework for VLA models, enhances long-horizon action planning, achieves state-of-the-art performance, and discovers novel patterns during training.
Dongfu Jiang, Yi Lu, Zhuofeng Li +9 authors
VerlTool is a unified and modular framework for Agentic Reinforcement Learning with Tool use, addressing inefficiencies in existing approaches and providing competitive performance across multiple domains.
Mengzhao Chen, Meng Wu, Hui Jin +10 authors
A comprehensive comparison of low-precision floating-point and integer quantization in large language models reveals that fine-grained integer formats, especially MXINT8, offer superior accuracy and efficiency over floating-point formats in many cases.
Luis Wiedmann, Orr Zohar, Amir Mahla +6 authors
FineVision, a large-scale and curated dataset, enhances vision-language models through rigorous data collection, de-duplication, and human oversight, leading to improved performance.
Hippolyte Gisserot-Boukhlef, Nicolas Boizard, Manuel Faysse +5 authors
A biphasic training strategy combining Causal Language Modeling and Masked Language Modeling yields optimal text representation performance, especially when initialized with pretrained CLM models.
Luke Rivard, Sun Sun, Hongyu Guo +2 authors
NeuralOS, a neural framework combining RNNs and diffusion-based rendering, simulates realistic GUIs by predicting screen frames in response to user inputs.
Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Duarte M. Alves +16 authors
EuroBERT, a family of multilingual encoders covering European and global languages, outperforms existing models across various tasks and supports long sequences, surpassing traditional bidirectional encoders.
Yi-Fan Zhang, Xingyu Lu, Shukang Yin +17 authors
Thyme, a novel paradigm, enables MLLMs to autonomously perform image manipulations and computations, enhancing performance in perception and reasoning tasks through a two-stage training strategy and GRPO-ATS algorithm.
Cong Wei, Quande Liu, Zixuan Ye +5 authors
UniVideo, a dual-stream framework combining a Multimodal Large Language Model and a Multimodal DiT, extends unified modeling to video generation and editing, achieving state-of-the-art performance and supporting task composition and generalization.
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.
Guoxin Chen, Zile Qiao, Xuanzhong Chen +13 authors
IterResearch, an iterative deep-research paradigm, improves long-horizon reasoning by reformulating it as a Markov Decision Process with strategic workspace reconstruction and Efficiency-Aware Policy Optimization, achieving better performance and interaction scaling compared to existing agents.
Tong Wu, Yang Liu, Jun Bai +6 authors
NPR, a teacher-free framework, enhances Large Language Models with native parallel reasoning capabilities through self-distilled training, Parallel-Aware Policy Optimization, and a robust NPR Engine, achieving substantial performance and speed improvements.
Senqiao Yang, Junyi Li, Xin Lai +3 authors
VisionThink dynamically adjusts image resolution and visual token processing for efficient and effective vision-language tasks, improving performance and reducing computational cost.
Yizhi Li, Qingshui Gu, Zhoufutu Wen +14 authors
TreePO, a self-guided rollout algorithm for sequence generation, reduces computational cost and enhances exploration diversity in reinforcement learning for large language models.
Yu Wang, Xi Chen
MIRIX, a modular multi-agent memory system, enhances language models' memory capabilities by integrating diverse memory types and a dynamic framework, achieving superior performance in multimodal and long-form conversation benchmarks.
Xixi Wu, Kuan Li, Yida Zhao +11 authors
ReSum, a novel paradigm with periodic context summarization, enhances web agents' performance on knowledge-intensive tasks by overcoming context window limitations, achieving significant improvements over ReAct.
Kaituo Feng, Kaixiong Gong, Bohao Li +5 authors
Video-R1, leveraging rule-based reinforcement learning and temporal information, enhances video reasoning in multimodal large language models using a combination of video and image data.
Guilherme Penedo, Hynek Kydlíček, Vinko Sabolčec +7 authors
A new pre-training dataset curation pipeline based on FineWeb supports multilingual LLMs by automatically adapting to any language, improving model performance and balancing dataset quality.
Advait Gupta, NandaKiran Velaga, Dang Nguyen +1 authors
A three-stage approach, CoSTA*, combines LLMs and A* search to efficiently find cost-effective tool paths for multi-turn text-to-image editing, outperforming state-of-the-art models in cost and quality.
Maggie Huan, Yuetai Li, Tuney Zheng +6 authors
Reinforcement learning-tuned models generalize better across domains compared to supervised fine-tuned models in reasoning tasks, indicating a need to reconsider standard training methods.
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.
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.
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.
Xiangpeng Yang, Linchao Zhu, Hehe Fan +1 authors
VideoGrain uses space-time attention mechanisms to enhance fine-grained video editing by improving text-to-region control and feature separation.
Chenyuan Wu, Pengfei Zheng, Ruiran Yan +19 authors
OmniGen2, a versatile generative model, introduces dual decoding pathways for text and images, preserves original text generation, and achieves competitive results with a new subject-driven benchmark.
Artem Chervyakov, Ulyana Isaeva, Anton Emelyanov +15 authors
Mera Multi is an open multimodal evaluation framework for Russian-spoken architectures, addressing the lack of such benchmarks with 18 newly constructed tasks and a methodology to prevent benchmark leakage.
Delin Qu, Haoming Song, Qizhi Chen +12 authors
EO-Robotics, comprising EO-1 model and EO-Data1.5M dataset, advances multimodal embodied reasoning and robot control through interleaved vision-text-action pre-training.
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