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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:2508.04700
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AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
Paper • 2402.15506 • Published • 18 -
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Paper • 2404.03648 • Published • 30 -
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Paper • 2405.19893 • Published • 33 -
Parrot: Efficient Serving of LLM-based Applications with Semantic Variable
Paper • 2405.19888 • Published • 7
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UI-AGILE: Advancing GUI Agents with Effective Reinforcement Learning and Precise Inference-Time Grounding
Paper • 2507.22025 • Published • 4 -
InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy Optimization
Paper • 2508.05731 • Published • 25 -
VeriGUI: Verifiable Long-Chain GUI Dataset
Paper • 2508.04026 • Published • 160 -
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience
Paper • 2508.04700 • Published • 52
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Towards General-Purpose Model-Free Reinforcement Learning
Paper • 2501.16142 • Published • 30 -
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Paper • 2503.14476 • Published • 142 -
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
Paper • 2504.13837 • Published • 138 -
Learning to Reason under Off-Policy Guidance
Paper • 2504.14945 • Published • 88
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Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Paper • 2508.03680 • Published • 121 -
Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning
Paper • 2508.03501 • Published • 59 -
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience
Paper • 2508.04700 • Published • 52 -
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong Learning in Physical Embodied Systems
Paper • 2508.01415 • Published • 7
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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 • 74 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277
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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
-
Towards General-Purpose Model-Free Reinforcement Learning
Paper • 2501.16142 • Published • 30 -
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Paper • 2503.14476 • Published • 142 -
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
Paper • 2504.13837 • Published • 138 -
Learning to Reason under Off-Policy Guidance
Paper • 2504.14945 • Published • 88
-
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
Paper • 2402.15506 • Published • 18 -
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Paper • 2404.03648 • Published • 30 -
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Paper • 2405.19893 • Published • 33 -
Parrot: Efficient Serving of LLM-based Applications with Semantic Variable
Paper • 2405.19888 • Published • 7
-
Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Paper • 2508.03680 • Published • 121 -
Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning
Paper • 2508.03501 • Published • 59 -
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience
Paper • 2508.04700 • Published • 52 -
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong Learning in Physical Embodied Systems
Paper • 2508.01415 • Published • 7
-
UI-AGILE: Advancing GUI Agents with Effective Reinforcement Learning and Precise Inference-Time Grounding
Paper • 2507.22025 • Published • 4 -
InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy Optimization
Paper • 2508.05731 • Published • 25 -
VeriGUI: Verifiable Long-Chain GUI Dataset
Paper • 2508.04026 • Published • 160 -
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience
Paper • 2508.04700 • Published • 52
-
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 • 74 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277