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Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Paper • 2412.18319 • Published • 39 -
Token-Budget-Aware LLM Reasoning
Paper • 2412.18547 • Published • 46 -
Efficiently Serving LLM Reasoning Programs with Certaindex
Paper • 2412.20993 • Published • 37 -
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
Paper • 2412.17256 • Published • 47
Collections
Discover the best community collections!
Collections including paper arxiv:2501.04682
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Visual-RFT: Visual Reinforcement Fine-Tuning
Paper • 2503.01785 • Published • 85 -
When an LLM is apprehensive about its answers -- and when its uncertainty is justified
Paper • 2503.01688 • Published • 21 -
Predictive Data Selection: The Data That Predicts Is the Data That Teaches
Paper • 2503.00808 • Published • 56 -
Chain of Draft: Thinking Faster by Writing Less
Paper • 2502.18600 • Published • 50
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Internal Consistency and Self-Feedback in Large Language Models: A Survey
Paper • 2407.14507 • Published • 46 -
Large Language Models are Zero-Shot Reasoners
Paper • 2205.11916 • Published • 3 -
Let's Verify Step by Step
Paper • 2305.20050 • Published • 11 -
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Paper • 2201.11903 • Published • 14
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rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 286 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 99 -
Search-o1: Agentic Search-Enhanced Large Reasoning Models
Paper • 2501.05366 • Published • 102 -
Agent Laboratory: Using LLM Agents as Research Assistants
Paper • 2501.04227 • Published • 95
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Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback
Paper • 2501.03916 • Published • 16 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 99 -
Agent Laboratory: Using LLM Agents as Research Assistants
Paper • 2501.04227 • Published • 95 -
Search-o1: Agentic Search-Enhanced Large Reasoning Models
Paper • 2501.05366 • Published • 102
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Training Software Engineering Agents and Verifiers with SWE-Gym
Paper • 2412.21139 • Published • 24 -
Evaluating Language Models as Synthetic Data Generators
Paper • 2412.03679 • Published • 48 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 117
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SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 249 -
Demystifying Long Chain-of-Thought Reasoning in LLMs
Paper • 2502.03373 • Published • 58 -
Kimi k1.5: Scaling Reinforcement Learning with LLMs
Paper • 2501.12599 • Published • 125 -
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Paper • 2501.17161 • Published • 123
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DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper • 2501.12948 • Published • 429 -
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Paper • 2502.05171 • Published • 151 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 99 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 286
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Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 286 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 99
-
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Paper • 2412.18319 • Published • 39 -
Token-Budget-Aware LLM Reasoning
Paper • 2412.18547 • Published • 46 -
Efficiently Serving LLM Reasoning Programs with Certaindex
Paper • 2412.20993 • Published • 37 -
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
Paper • 2412.17256 • Published • 47
-
Training Software Engineering Agents and Verifiers with SWE-Gym
Paper • 2412.21139 • Published • 24 -
Evaluating Language Models as Synthetic Data Generators
Paper • 2412.03679 • Published • 48 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 117
-
Visual-RFT: Visual Reinforcement Fine-Tuning
Paper • 2503.01785 • Published • 85 -
When an LLM is apprehensive about its answers -- and when its uncertainty is justified
Paper • 2503.01688 • Published • 21 -
Predictive Data Selection: The Data That Predicts Is the Data That Teaches
Paper • 2503.00808 • Published • 56 -
Chain of Draft: Thinking Faster by Writing Less
Paper • 2502.18600 • Published • 50
-
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 249 -
Demystifying Long Chain-of-Thought Reasoning in LLMs
Paper • 2502.03373 • Published • 58 -
Kimi k1.5: Scaling Reinforcement Learning with LLMs
Paper • 2501.12599 • Published • 125 -
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Paper • 2501.17161 • Published • 123
-
Internal Consistency and Self-Feedback in Large Language Models: A Survey
Paper • 2407.14507 • Published • 46 -
Large Language Models are Zero-Shot Reasoners
Paper • 2205.11916 • Published • 3 -
Let's Verify Step by Step
Paper • 2305.20050 • Published • 11 -
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Paper • 2201.11903 • Published • 14
-
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper • 2501.12948 • Published • 429 -
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Paper • 2502.05171 • Published • 151 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 99 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 286
-
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 286 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 99 -
Search-o1: Agentic Search-Enhanced Large Reasoning Models
Paper • 2501.05366 • Published • 102 -
Agent Laboratory: Using LLM Agents as Research Assistants
Paper • 2501.04227 • Published • 95
-
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 286 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 99
-
Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback
Paper • 2501.03916 • Published • 16 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 99 -
Agent Laboratory: Using LLM Agents as Research Assistants
Paper • 2501.04227 • Published • 95 -
Search-o1: Agentic Search-Enhanced Large Reasoning Models
Paper • 2501.05366 • Published • 102