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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collections
Discover the best community collections!
Collections including paper arxiv:2505.24726
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Why Language Models Hallucinate
Paper • 2509.04664 • Published • 195 -
BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
Paper • 2508.21184 • Published • 2 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Small Language Models are the Future of Agentic AI
Paper • 2506.02153 • Published • 23
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Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models
Paper • 2506.06395 • Published • 133 -
Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
Paper • 2506.05176 • Published • 77 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277
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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 • 250 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 259
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Snowflake/Arctic-Text2SQL-R1-7B
8B • Updated • 970 • 57 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
Paper • 2506.16406 • Published • 130
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
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 • 250 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 259
-
Why Language Models Hallucinate
Paper • 2509.04664 • Published • 195 -
BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
Paper • 2508.21184 • Published • 2 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Small Language Models are the Future of Agentic AI
Paper • 2506.02153 • Published • 23
-
Snowflake/Arctic-Text2SQL-R1-7B
8B • Updated • 970 • 57 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
Paper • 2506.16406 • Published • 130
-
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models
Paper • 2506.06395 • Published • 133 -
Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
Paper • 2506.05176 • Published • 77 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277