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Jeffrey Amico, Gabriel Passamani Andrade, John Donaghy +12 authors
Swarm sAmpling Policy Optimization (SAPO) is a decentralized and asynchronous RL algorithm that enhances post-training language models without supervised fine-tuning, achieving significant reward gains and scalability across diverse hardware.
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Adrian Kosowski, Przemysław Uznański, Jan Chorowski +2 authors
BDH, a biologically inspired Large Language Model, combines scale-free network architecture with Hebbian learning to achieve Transformer-like performance while maintaining interpretability.
Alexia Jolicoeur-Martineau
Tiny Recursive Model (TRM) achieves high generalization on complex puzzle tasks using a small, two-layer network with minimal parameters, outperforming larger language models.
DeepSeek-AI, Daya Guo, Dejian Yang +197 authors
DeepSeek-R1-Zero and DeepSeek-R1 utilize reinforcement learning and multi-stage training to enhance reasoning capabilities, with DeepSeek-R1 achieving performance comparable to OpenAI-o1-1217.
Keke Lian, Bin Wang, Lei Zhang +18 authors
A.S.E is a repository-level benchmark for evaluating the security of AI-generated code, highlighting challenges in secure coding and the limitations of LLMs in real-world scenarios.
An Yang, Anfeng Li, Baosong Yang +57 authors
Qwen3, a unified series of large language models, integrates thinking and non-thinking modes, reduces computational resources, and achieves state-of-the-art performance across various tasks and languages.
Zhenda Xie, Yixuan Wei, Huanqi Cao +16 authors
Manifold-Constrained Hyper-Connections (mHC) stabilize and scale residual connection architectures by restoring identity mapping properties through manifold projection and infrastructure optimization.
Chujie Zheng, Shixuan Liu, Mingze Li +9 authors
Group Sequence Policy Optimization (GSPO) is a reinforcement learning algorithm that improves training efficiency and performance of large language models by using sequence-level importance ratios and operations.
Oriane Siméoni, Huy V. Vo, Maximilian Seitzer +23 authors
DINOv3, a self-supervised learning model, achieves superior performance across various vision tasks by scaling datasets and models, addressing dense feature degradation, and enhancing flexibility with post-hoc strategies.
Jinguo Zhu, Weiyun Wang, Zhe Chen +44 authors
InternVL3 is a multimodal pre-trained language model that jointly learns from both multimodal data and text, improving performance and scalability through advanced techniques and setting a new state-of-the-art in multimodal tasks.
Jian Yang, Xianglong Liu, Weifeng Lv +68 authors
A comprehensive guide to code LLMs, covering their lifecycle from data curation to deployment, including techniques, trade-offs, and research-practice gaps.
Bang Liu, Xinfeng Li, Jiayi Zhang +44 authors
This survey covers the design, evaluation, and improvement of intelligent agents based on modular, brain-inspired architectures, focusing on self-enhancement, multi-agent collaboration, and safety in AI systems.
MiniMax, Aonian Li, Bangwei Gong +87 authors
The MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, offer superior long-context processing and match state-of-the-art model performance with longer context windows through lightning attention, Mixture of Experts (MoE), and efficient parallel strategies.
Xinyu Guan, Li Lyna Zhang, Yifei Liu +5 authors
rStar-Math enhances small language models' math reasoning capabilities through Monte Carlo Tree Search and self-evolution, achieving state-of-the-art performance on various benchmarks without distillation from larger models.
Shelly Bensal, Umar Jamil, Christopher Bryant +5 authors
A method using self-reflection and reinforcement learning improves the performance of large language models, especially with limited feedback, by rewarding self-reflections that lead to better task performance.
MiniMax, Aili Chen, Aonian Li +124 authors
A hybrid-attention reasoning model called MiniMax-M1, featuring a Mixture-of-Experts architecture and lightning attention mechanism, is introduced for efficient long-input processing and reinforcement learning.
Kai Zhang, Xiangchao Chen, Bo Liu +27 authors
Early experience, using agent-generated interaction data without reward signals, improves policy effectiveness and generalization, serving as a bridge between imitation learning and reinforcement learning.
Chenfei Wu, Jiahao Li, Jingren Zhou +36 authors
Qwen-Image, an image generation model, advances text rendering and image editing through a comprehensive data pipeline, progressive training, and dual-encoding mechanism.
Lei Bai, Zhongrui Cai, Maosong Cao +172 authors
Intern-S1, a multimodal Mixture-of-Experts model with extensive pre-training and reinforcement learning, achieves top-tier performance in general reasoning and outperforms closed-source models in scientific tasks.
DeepSeek-AI, Aixin Liu, Aoxue Mei +261 authors
DeepSeek-V3.2 introduces DeepSeek Sparse Attention and a scalable reinforcement learning framework, achieving superior reasoning and performance compared to GPT-5 and Gemini-3.0-Pro in complex reasoning tasks.
Qingxiu Dong, Li Dong, Yao Tang +4 authors
Reinforcement Pre-Training (RPT) improves language model accuracy through reinforcement learning and offers a scalable method for leveraging text data for general-purpose RL.
Lingrui Mei, Jiayu Yao, Yuyao Ge +12 authors
Context Engineering systematically optimizes information payloads for Large Language Models, addressing gaps in generating sophisticated, long-form outputs.
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.
Yihao Wang, Pengxiang Ding, Lingxiao Li +13 authors
VLA-Adapter reduces reliance on large-scale VLMs and extensive pre-training by using a lightweight Policy module with Bridge Attention, achieving state-of-the-art performance and fast inference speed with minimal computational resources.
Wenyi Hong, Wenmeng Yu, Xiaotao Gu +74 authors
A vision-language model (VLM) named GLM-4.1V-Thinking, developed with a reasoning-centric training framework, achieves state-of-the-art performance across various tasks, including STEM problem solving, video understanding, and long document understanding, outperforming larger models on many benchmarks.
Z-Image Team, Huanqia Cai, Sihan Cao +18 authors
Z-Image, a 6B-parameter Scalable Single-Stream Diffusion Transformer (S3-DiT) model, achieves high-performance image generation with reduced computational cost, offering sub-second inference and compatibility with consumer hardware.
Jingqi Tong, Yurong Mou, Hangcheng Li +11 authors
The "Thinking with Video" paradigm enhances multimodal reasoning by integrating video generation models, demonstrated through the Video Thinking Benchmark and improved performance on both vision and text tasks.
Chengshuai Zhao, Zhen Tan, Pingchuan Ma +5 authors
CoT reasoning in LLMs is found to be limited by the distribution discrepancy between training and test data, suggesting it is not a robust form of reasoning.
Guibin Zhang, Hejia Geng, Xiaohang Yu +22 authors
Agentic reinforcement learning transforms large language models into autonomous decision-making agents by leveraging temporally extended POMDPs, enhancing capabilities like planning and reasoning through reinforcement learning.
Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko +22 authors
Kandinsky 5.0 is a family of state-of-the-art generative models for high-resolution images and short videos, featuring model lineups with varying parameters and enhanced training techniques to achieve superior quality and performance.
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