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Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Paper • 2508.03680 • Published • 122 -
A Comprehensive Survey on Self-Interpretable Neural Networks
Paper • 2501.15638 • Published • 2 -
Continuous Autoregressive Language Models
Paper • 2510.27688 • Published • 70 -
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Paper • 2510.16872 • Published • 106
Collections
Discover the best community collections!
Collections including paper arxiv:2403.13372
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WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
Paper • 2507.15061 • Published • 60 -
WebDancer: Towards Autonomous Information Seeking Agency
Paper • 2505.22648 • Published • 33 -
ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization
Paper • 2509.13313 • Published • 80 -
WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning
Paper • 2509.13305 • Published • 91
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LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 176 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 53 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 259
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Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing
Paper • 2509.08721 • Published • 661 -
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
Paper • 2508.18106 • Published • 347 -
VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
Paper • 2509.09372 • Published • 243 -
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Paper • 2509.02547 • Published • 228
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AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 160 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 176 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 43 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 53
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AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 160 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 176 -
LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations
Paper • 2509.03405 • Published • 23 -
KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing Applications
Paper • 2503.17247 • Published • 1
-
Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Paper • 2508.03680 • Published • 122 -
A Comprehensive Survey on Self-Interpretable Neural Networks
Paper • 2501.15638 • Published • 2 -
Continuous Autoregressive Language Models
Paper • 2510.27688 • Published • 70 -
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Paper • 2510.16872 • Published • 106
-
Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing
Paper • 2509.08721 • Published • 661 -
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
Paper • 2508.18106 • Published • 347 -
VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
Paper • 2509.09372 • Published • 243 -
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Paper • 2509.02547 • Published • 228
-
WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
Paper • 2507.15061 • Published • 60 -
WebDancer: Towards Autonomous Information Seeking Agency
Paper • 2505.22648 • Published • 33 -
ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization
Paper • 2509.13313 • Published • 80 -
WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning
Paper • 2509.13305 • Published • 91
-
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 160 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 176 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 43 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 53
-
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 176 -
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Paper • 2508.09834 • Published • 53 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 259
-
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 160 -
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Paper • 2403.13372 • Published • 176 -
LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations
Paper • 2509.03405 • Published • 23 -
KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing Applications
Paper • 2503.17247 • Published • 1