11 papers
LUT: Latent Utility Training for Visual Reasoning
Jiaxuan Kang, Siyu Chen, Mingda Li +6
Multimodal large language models have advanced visual understanding, yet perception-intensive reasoning remains challenging. Recent latent visual reasoning methods introduce hidden…
ProCUA-SFT Technical Report
Jaehun Jung, Ximing Lu, Brandon Cui +11
Training computer-use agents (CUAs) -- models that interact with graphical desktops through screenshots and keyboard/mouse actions -- requires large-scale, diverse trajectory data…
Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
NVIDIA, :, Amala Sanjay Deshmukh +204
We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…
ProRL Agent: Rollout-as-a-Service for RL Training of Multi-Turn LLM Agents
Hao Zhang, Mingjie Liu, Shaokun Zhang +10
Multi-turn LLM agents are increasingly important for solving complex, interactive tasks, and reinforcement learning (RL) is a key ingredient for improving their long-horizon behavi…
Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text
Ximing Lu, David Acuna, Jaehun Jung +12
Reinforcement Learning with Verifiable Rewards (RLVR) has become a cornerstone for unlocking complex reasoning in Large Language Models (LLMs). Yet, scaling up RL is bottlenecked b…
GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
Shih-Yang Liu, Xin Dong, Ximing Lu +10
As language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety…
ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
Hongjin Su, Shizhe Diao, Ximing Lu +13
Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity's Last Exam (HLE) remains both conceptually challenging and comp…
DLER: Doing Length pEnalty Right - Incentivizing More Intelligence per Token via Reinforcement Learning
Shih-Yang Liu, Xin Dong, Ximing Lu +9
Reasoning language models such as OpenAI-o1, DeepSeek-R1, and Qwen achieve strong performance via extended chains of thought but often generate unnecessarily long outputs. Maximizi…
BroRL: Scaling Reinforcement Learning via Broadened Exploration
Jian Hu, Mingjie Liu, Ximing Lu +8
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key ingredient for unlocking complex reasoning capabilities in large language models. Recent work ProRL has s…
The Invisible Leash: Why RLVR May or May Not Escape Its Origin
Fang Wu, Weihao Xuan, Danny Xing +5
Recent advances highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising approach to enhancing LLM capabilities. However, it remains unclear whether the curre…
ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
Mingjie Liu, Shizhe Diao, Ximing Lu +5
Recent advances in reasoning-centric language models have highlighted reinforcement learning (RL) as a promising method for aligning models with verifiable rewards. However, it rem…