Contrastive Decoding Improves Reasoning in Large Language Models
Paper
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2309.09117
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Published
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39
Reasoning
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Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
Paper • 2310.08491 • Published • 55 -
Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding
Paper • 2411.04282 • Published • 36 -
Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models
Paper • 2411.14432 • Published • 26 -
Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning
Paper • 2412.15797 • Published • 18 -
Search-o1: Agentic Search-Enhanced Large Reasoning Models
Paper • 2501.05366 • Published • 101 -
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning
Paper • 2502.03275 • Published • 18 -
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Paper • 2502.05171 • Published • 142 -
One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs
Paper • 2502.10454 • Published • 7 -
I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models
Paper • 2502.10458 • Published • 36 -
Entropy-Regularized Process Reward Model
Paper • 2412.11006 • Published -
What Are Step-Level Reward Models Rewarding? Counterintuitive Findings from MCTS-Boosted Mathematical Reasoning
Paper • 2412.15904 • Published -
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
Paper • 2503.05592 • Published • 27 -
GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing
Paper • 2503.10639 • Published • 50 -
GTR: Guided Thought Reinforcement Prevents Thought Collapse in RL-based VLM Agent Training
Paper • 2503.08525 • Published • 17 -
OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-Improvement
Paper • 2503.17352 • Published • 23 -
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models
Paper • 2503.24235 • Published • 54 -
Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback
Paper • 2503.22230 • Published • 44 -
Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
Paper • 2503.24290 • Published • 62 -
VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks
Paper • 2504.05118 • Published • 25 -
FlowReasoner: Reinforcing Query-Level Meta-Agents
Paper • 2504.15257 • Published • 46 -
Reward Reasoning Model
Paper • 2505.14674 • Published • 35 -
Think Only When You Need with Large Hybrid-Reasoning Models
Paper • 2505.14631 • Published • 19 -
Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
Paper • 2505.19914 • Published • 42 -
VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy Optimization
Paper • 2505.19000 • Published • 42 -
The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models
Paper • 2505.22617 • Published • 120 -
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Paper • 2505.24863 • Published • 91 -
From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval
Paper • 2505.23059 • Published • 13 -
OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for Over-Reasoning Mitigation
Paper • 2506.02397 • Published • 36 -
Comment on The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
Paper • 2506.09250 • Published • 21