Trending Research
Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning
volcengine/verl
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We propose Rec-R1, a general reinforcement learning framework that bridges large language models (LLMs) with recommendation systems through closed-loop optimization.
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Efficiently acquiring external knowledge and up-to-date information is essential for effective reasoning and text generation in large language models (LLMs).
VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning
Notably, through visual instruction tuning, the model acquires image editing functionality while maintaining architectural consistency with its predecessor, revealing the potential for unified visual understanding, generation, and editing.
MedSAM2: Segment Anything in 3D Medical Images and Videos
Medical image and video segmentation is a critical task for precision medicine, which has witnessed considerable progress in developing task or modality-specific and generalist models for 2D images.
Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving
The task of issue resolving is to modify a codebase to generate a patch that addresses a given issue.
VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
om-ai-lab/vlm-r1
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Motivated by this observation, we investigate the extension of R1-style reinforcement learning to Vision-Language Models (VLMs), aiming to enhance their visual reasoning capabilities.
Boosting the Generalization and Reasoning of Vision Language Models with Curriculum Reinforcement Learning
While state-of-the-art vision-language models (VLMs) have demonstrated remarkable capabilities in complex visual-text tasks, their success heavily relies on massive model scaling, limiting their practical deployment.
Perception-R1: Pioneering Perception Policy with Reinforcement Learning
linkangheng/pr1
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In this work, we return to the fundamentals and explore the effects of RL on different perception tasks.
Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning
GMC-DRL/MetaBox
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Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers, leverage a group of individuals to cooperatively search for the optimum.
HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation
To effectively learn the HealthGPT, we devise a comprehensive medical domain-specific comprehension and generation dataset called VL-Health.
