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Collections including paper arxiv:2508.21148
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The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Paper • 2509.02547 • Published • 225 -
A Survey of Reinforcement Learning for Large Reasoning Models
Paper • 2509.08827 • Published • 189 -
A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Paper • 2508.21148 • Published • 140
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 626 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 301 -
Group Sequence Policy Optimization
Paper • 2507.18071 • Published • 314 -
Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Paper • 2509.03867 • Published • 211
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A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Paper • 2508.21148 • Published • 140 -
A Survey of Reinforcement Learning for Large Reasoning Models
Paper • 2509.08827 • Published • 189 -
Architecture Decoupling Is Not All You Need For Unified Multimodal Model
Paper • 2511.22663 • Published • 28
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Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
Paper • 2508.08221 • Published • 49 -
Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models
Paper • 2508.02120 • Published • 19 -
Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
Paper • 2506.23918 • Published • 89 -
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Paper • 2509.02547 • Published • 225
-
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
Paper • 2507.01006 • Published • 240 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 259
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Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 18 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
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ComfyUI-R1: Exploring Reasoning Models for Workflow Generation
Paper • 2506.09790 • Published • 53 -
Saffron-1: Towards an Inference Scaling Paradigm for LLM Safety Assurance
Paper • 2506.06444 • Published • 73 -
DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
Paper • 2506.11763 • Published • 72 -
Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research
Paper • 2502.04644 • Published • 4
-
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
Paper • 2507.01006 • Published • 240 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 259
-
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Paper • 2509.02547 • Published • 225 -
A Survey of Reinforcement Learning for Large Reasoning Models
Paper • 2509.08827 • Published • 189 -
A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Paper • 2508.21148 • Published • 140
-
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 626 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 301 -
Group Sequence Policy Optimization
Paper • 2507.18071 • Published • 314 -
Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Paper • 2509.03867 • Published • 211
-
A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Paper • 2508.21148 • Published • 140 -
A Survey of Reinforcement Learning for Large Reasoning Models
Paper • 2509.08827 • Published • 189 -
Architecture Decoupling Is Not All You Need For Unified Multimodal Model
Paper • 2511.22663 • Published • 28
-
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 18 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
-
Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
Paper • 2508.08221 • Published • 49 -
Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models
Paper • 2508.02120 • Published • 19 -
Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
Paper • 2506.23918 • Published • 89 -
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Paper • 2509.02547 • Published • 225
-
ComfyUI-R1: Exploring Reasoning Models for Workflow Generation
Paper • 2506.09790 • Published • 53 -
Saffron-1: Towards an Inference Scaling Paradigm for LLM Safety Assurance
Paper • 2506.06444 • Published • 73 -
DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
Paper • 2506.11763 • Published • 72 -
Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research
Paper • 2502.04644 • Published • 4