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Collections including paper arxiv:2511.17592
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Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
Paper • 2509.07980 • Published • 101 -
Robot Learning from a Physical World Model
Paper • 2511.07416 • Published • 30 -
MathSE: Improving Multimodal Mathematical Reasoning via Self-Evolving Iterative Reflection and Reward-Guided Fine-Tuning
Paper • 2511.06805 • Published • 12 -
GigaEvo: An Open Source Optimization Framework Powered By LLMs And Evolution Algorithms
Paper • 2511.17592 • Published • 118
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lusxvr/nanoVLM-222M
Image-Text-to-Text • 0.2B • Updated • 523 • 98 -
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Paper • 2503.09516 • Published • 36 -
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Paper • 2505.24863 • Published • 97 -
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
Paper • 2505.17667 • Published • 88
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Statistical Methods in Generative AI
Paper • 2509.07054 • Published • 11 -
Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
Paper • 2509.07980 • Published • 101 -
Agent Learning via Early Experience
Paper • 2510.08558 • Published • 266 -
GigaEvo: An Open Source Optimization Framework Powered By LLMs And Evolution Algorithms
Paper • 2511.17592 • Published • 118
-
Compositional Foundation Models for Hierarchical Planning
Paper • 2309.08587 • Published • 11 -
ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models
Paper • 2405.09220 • Published • 28 -
WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents
Paper • 2504.15785 • Published • 22 -
CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning
Paper • 2508.20096 • Published • 36
-
Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
Paper • 2509.07980 • Published • 101 -
Robot Learning from a Physical World Model
Paper • 2511.07416 • Published • 30 -
MathSE: Improving Multimodal Mathematical Reasoning via Self-Evolving Iterative Reflection and Reward-Guided Fine-Tuning
Paper • 2511.06805 • Published • 12 -
GigaEvo: An Open Source Optimization Framework Powered By LLMs And Evolution Algorithms
Paper • 2511.17592 • Published • 118
-
Statistical Methods in Generative AI
Paper • 2509.07054 • Published • 11 -
Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
Paper • 2509.07980 • Published • 101 -
Agent Learning via Early Experience
Paper • 2510.08558 • Published • 266 -
GigaEvo: An Open Source Optimization Framework Powered By LLMs And Evolution Algorithms
Paper • 2511.17592 • Published • 118
-
lusxvr/nanoVLM-222M
Image-Text-to-Text • 0.2B • Updated • 523 • 98 -
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Paper • 2503.09516 • Published • 36 -
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Paper • 2505.24863 • Published • 97 -
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
Paper • 2505.17667 • Published • 88
-
Compositional Foundation Models for Hierarchical Planning
Paper • 2309.08587 • Published • 11 -
ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models
Paper • 2405.09220 • Published • 28 -
WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents
Paper • 2504.15785 • Published • 22 -
CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning
Paper • 2508.20096 • Published • 36