Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models
Paper • 2508.10751 • Published • 29
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Reinforcement Pre-Training
Paper • 2506.08007 • Published • 265 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 43 -
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 162 -
AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Paper • 2508.16279 • Published • 61 -
Are LLM-Judges Robust to Expressions of Uncertainty? Investigating the effect of Epistemic Markers on LLM-based Evaluation
Paper • 2410.20774 • Published -
Provable Benefits of In-Tool Learning for Large Language Models
Paper • 2508.20755 • Published • 11 -
Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI Agents
Paper • 2509.06917 • Published • 44 -
RLP: Reinforcement as a Pretraining Objective
Paper • 2510.01265 • Published • 45 -
Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Paper • 2508.03680 • Published • 140 -
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Paper • 2510.16872 • Published • 112 -
Scaling Latent Reasoning via Looped Language Models
Paper • 2510.25741 • Published • 229 -
Emu3.5: Native Multimodal Models are World Learners
Paper • 2510.26583 • Published • 114 -
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models
Paper • 2601.07372 • Published • 47