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Loubna Ben Allal, Anton Lozhkov, Elie Bakouch +19 authors
SmolLM2, a small language model with 1.7 billion parameters, achieves strong performance through overtraining on diverse datasets, outperforming other recent small models.
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Gaojie Lin, Jianwen Jiang, Jiaqi Yang +2 authors
OmniHuman, a Diffusion Transformer-based framework, achieves realistic human video generation by integrating motion-related conditions into its training process, supporting various inputs and driving modalities.
Shuai Bai, Keqin Chen, Xuejing Liu +24 authors
Qwen2.5-VL, the latest vision-language model, advances visual recognition, document parsing, and video comprehension through dynamic resolution processing, Window Attention, and a native Vision Transformer.
Deepak Nathani, Lovish Madaan, Nicholas Roberts +14 authors
A new framework and benchmark, MLGym and MLGym-Bench, is introduced for evaluating and developing LLM agents across diverse AI research tasks using reinforcement learning.
Mo Yu, Lemao Liu, Junjie Wu +5 authors
A study investigates whether large language models understand physical concepts through a grid-based task, showing that they significantly underperform compared to humans and highlighting the stochastic parrot phenomenon.
Anton Razzhigaev, Matvey Mikhalchuk, Temurbek Rahmatullaev +4 authors
Analysis of Large Language Models reveals that even minor tokens are crucial for context, and introduces LLM-Microscope for assessing token-level nonlinearity and contextual memory.
Jingyang Yuan, Huazuo Gao, Damai Dai +12 authors
NSA, a trainable sparse attention mechanism, enhances long-context modeling efficiency without sacrificing performance, achieving improvements in speed and accuracy over full attention models.
Michael Tschannen, Alexey Gritsenko, Xiao Wang +11 authors
SigLIP 2, a multilingual vision-language encoder, improves upon SigLIP with unified training techniques, enhancing performance in zero-shot classification, image-text retrieval, localization, and dense prediction across various model sizes and data diversity.
Jonas Geiping, Sean McLeish, Neel Jain +6 authors
A novel language model scales test-time computation by reasoning in latent space, outperforming token-based models on reasoning benchmarks.
Runze Liu, Junqi Gao, Jian Zhao +5 authors
Test-Time Scaling improves Large Language Model performance by optimizing inference computation based on policy models, Process Reward Models, and problem difficulty.
Heejun Lee, Geon Park, Jaduk Suh +1 authors
InfiniteHiP is a novel LLM inference framework that enhances long-context processing with token pruning, RoPE adjustment, and host memory offloading, enabling faster and more memory-efficient operation.
Kiran Kamble, Melisa Russak, Dmytro Mozolevskyi +3 authors
FailSafeQA evaluates the robustness and context-awareness of large language models in financial applications through domain expertise, query completeness, linguistic accuracy, and document relevance challenges.
Shen Nie, Fengqi Zhu, Zebin You +7 authors
LLaDA, a diffusion model trained from scratch, outperforms autoregressive models in benchmarks and demonstrates strong instruction-following capabilities, challenging the dominance of ARMs in LLMs.
Niklas Muennighoff, Zitong Yang, Weijia Shi +7 authors
The use of budget forcing during test time improves reasoning performance in language models, as demonstrated by the s1 model which outperforms the o1-preview on competition math questions.
Alexey Gorbatovski, Boris Shaposhnikov, Viacheslav Sinii +2 authors
Direct Alignment Algorithms improve language model alignment by introducing a supervised fine-tuning phase and adjusting preference optimization strength, showing that ranking objectives are crucial for performance.
M-A-P Team, Xinrun Du, Yifan Yao +92 authors
SuperGPQA, a benchmark evaluating LLMs across 285 disciplines, reveals performance gaps and offers insights into the collaborative filtering process with human experts.
Shoufa Chen, Chongjian Ge, Yuqi Zhang +19 authors
Goku, a state-of-the-art family of joint image-and-video generation models using rectified flow Transformers, sets new benchmarks in text-to-image and text-to-video tasks.
Xun Liang, Jiawei Yang, Yezhaohui Wang +9 authors
SurveyX enhances automated survey generation through innovative preprocessing and post-processing methods, improving content and citation quality beyond existing systems.
Sergey Pletenev, Maria Marina, Daniil Moskovskiy +4 authors
Low-rank adaptation (LoRA) effectively updates LLMs with new knowledge but risks degrading performance on external benchmarks due to data bias and overconfidence.
Daniil Moskovskiy, Nikita Sushko, Sergey Pletenev +2 authors
A pipeline generates multilingual synthetic detoxification data using LLMs, outperforming models trained on human-annotated datasets in few-shot settings.
Yuhao Zhang, Zhiheng Liu, Fan Bu +3 authors
Soundwave addresses the representation space gap and sequence length inconsistency in end-to-end speech large language models using an efficient training strategy and novel architecture, outperforming Qwen2-Audio with significantly less data.
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.
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.
Yuxiang Wei, Olivier Duchenne, Jade Copet +6 authors
SWE-RL leverages reinforcement learning to enhance large language models' software engineering reasoning, achieving state-of-the-art performance and generalized reasoning skills by learning from extensive open-source software evolution data.
Xiangyu Zhao, Shengyuan Ding, Zicheng Zhang +10 authors
OmniAlign-V improves human preference alignment in MLLMs through supervised fine-tuning and preference optimization without compromising performance on VQA benchmarks.
Yuri Kuratov, Mikhail Arkhipov, Aydar Bulatov +1 authors
Using per-sample optimization, compression ratios of up to x1500 are achieved for sequence-to-vector compression in language models, highlighting a large gap between theoretical and practical limits.
Xiaoran Liu, Ruixiao Li, Mianqiu Huang +10 authors
The survey examines the advancements and challenges in long-context Large Language Models (LLMs), exploring the architecture, infrastructure, training, and evaluation technologies needed to extend their context length and address the inherent trade-offs.
Gallil Maimon, Avishai Elmakies, Yossi Adi
A method for training high-quality Speech Language Models on a single GPU within 24 hours, demonstrating cost-effectiveness and outperforming predicted computational scaling laws.
Fanxu Meng, Zengwei Yao, Muhan Zhang
TransMLA converts GQA-based models to MLA to reduce KV cache size and improve efficiency without sacrificing expressiveness.
OpenAI, Ahmed El-Kishky, Alexander Wei +22 authors
General-purpose reinforcement learning applied to large language models outperforms domain-specific systems in complex coding and reasoning tasks, achieving top results in competitions without hand-crafted strategies.
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