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Collections including paper arxiv:2508.20931

PVPO: Pre-Estimated Value-Based Policy Optimization for Agentic Reasoning

Paper • 2508.21104 • Published Aug 28 • 35
T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables

Paper • 2508.19813 • Published Aug 27 • 26
No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes

Paper • 2508.19060 • Published Aug 26 • 11
From reactive to cognitive: brain-inspired spatial intelligence for embodied agents

Paper • 2508.17198 • Published Aug 24 • 9

Provable Benefits of In-Tool Learning for Large Language Models

Paper • 2508.20755 • Published Aug 28 • 11
SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning

Paper • 2509.02479 • Published Sep 2 • 83
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-bench

Paper • 2508.20931 • Published Aug 28 • 15
THOR: Tool-Integrated Hierarchical Optimization via RL for Mathematical Reasoning

Paper • 2509.13761 • Published Sep 17 • 16

Agent Lightning: Train ANY AI Agents with Reinforcement Learning

Paper • 2508.03680 • Published Aug 5 • 121
Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning

Paper • 2508.03501 • Published Aug 5 • 59
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience

Paper • 2508.04700 • Published Aug 6 • 52
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong Learning in Physical Embodied Systems

Paper • 2508.01415 • Published Aug 2 • 7

How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-bench

Paper • 2508.20931 • Published Aug 28 • 15

Provable Benefits of In-Tool Learning for Large Language Models

Paper • 2508.20755 • Published Aug 28 • 11
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

Paper • 2508.20453 • Published Aug 28 • 63
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-bench

Paper • 2508.20931 • Published Aug 28 • 15
AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning

Paper • 2509.08755 • Published Sep 10 • 56

about 17 hours ago

lusxvr/nanoVLM-222M

Image-Text-to-Text • 0.2B • Updated May 8 • 510 • 98
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Paper • 2503.09516 • Published Mar 12 • 36
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

Paper • 2505.24863 • Published May 30 • 97
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning

Paper • 2505.17667 • Published May 23 • 88

PVPO: Pre-Estimated Value-Based Policy Optimization for Agentic Reasoning

Paper • 2508.21104 • Published Aug 28 • 35
T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables

Paper • 2508.19813 • Published Aug 27 • 26
No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes

Paper • 2508.19060 • Published Aug 26 • 11
From reactive to cognitive: brain-inspired spatial intelligence for embodied agents

Paper • 2508.17198 • Published Aug 24 • 9

How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-bench

Paper • 2508.20931 • Published Aug 28 • 15

Provable Benefits of In-Tool Learning for Large Language Models

Paper • 2508.20755 • Published Aug 28 • 11
SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning

Paper • 2509.02479 • Published Sep 2 • 83
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-bench

Paper • 2508.20931 • Published Aug 28 • 15
THOR: Tool-Integrated Hierarchical Optimization via RL for Mathematical Reasoning

Paper • 2509.13761 • Published Sep 17 • 16

Provable Benefits of In-Tool Learning for Large Language Models

Paper • 2508.20755 • Published Aug 28 • 11
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

Paper • 2508.20453 • Published Aug 28 • 63
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-bench

Paper • 2508.20931 • Published Aug 28 • 15
AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning

Paper • 2509.08755 • Published Sep 10 • 56

Agent Lightning: Train ANY AI Agents with Reinforcement Learning

Paper • 2508.03680 • Published Aug 5 • 121
Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning

Paper • 2508.03501 • Published Aug 5 • 59
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience

Paper • 2508.04700 • Published Aug 6 • 52
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong Learning in Physical Embodied Systems

Paper • 2508.01415 • Published Aug 2 • 7

about 17 hours ago

lusxvr/nanoVLM-222M

Image-Text-to-Text • 0.2B • Updated May 8 • 510 • 98
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Paper • 2503.09516 • Published Mar 12 • 36
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

Paper • 2505.24863 • Published May 30 • 97
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning

Paper • 2505.17667 • Published May 23 • 88

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