Collections

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Large Language Models Can Self-Improve in Long-context Reasoning

Paper • 2411.08147 • Published Nov 12, 2024 • 67
Reverse Thinking Makes LLMs Stronger Reasoners

Paper • 2411.19865 • Published Nov 29, 2024 • 23
Training Large Language Models to Reason in a Continuous Latent Space

Paper • 2412.06769 • Published Dec 9, 2024 • 82
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

Paper • 2412.18925 • Published Dec 25, 2024 • 101

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Evolving Deeper LLM Thinking

Paper • 2501.09891 • Published Jan 17 • 114
PaSa: An LLM Agent for Comprehensive Academic Paper Search

Paper • 2501.10120 • Published Jan 17 • 49
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong

Paper • 2501.09775 • Published Jan 16 • 34
ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling under Long-Context Scenario

Paper • 2501.10132 • Published Jan 17 • 20

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Large Language Models Can Self-Improve in Long-context Reasoning

Reverse Thinking Makes LLMs Stronger Reasoners

Training Large Language Models to Reason in a Continuous Latent Space

HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

Evolving Deeper LLM Thinking

PaSa: An LLM Agent for Comprehensive Academic Paper Search

Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong

ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling under Long-Context Scenario

Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs

Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization

Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedback

Reward-Guided Speculative Decoding for Efficient LLM Reasoning

Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedback

rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Agent Laboratory: Using LLM Agents as Research Assistants

Evolving Deeper LLM Thinking

ProcessBench: Identifying Process Errors in Mathematical Reasoning

AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling

The Lessons of Developing Process Reward Models in Mathematical Reasoning

Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

Token-Budget-Aware LLM Reasoning

Efficiently Serving LLM Reasoning Programs with Certaindex

B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners

RL Zero: Zero-Shot Language to Behaviors without any Supervision

Offline Reinforcement Learning for LLM Multi-Step Reasoning

Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning

Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning

Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models

Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level

StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization

Rho-1: Not All Tokens Are What You Need

VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time

Instruction-tuned Language Models are Better Knowledge Learners

DoRA: Weight-Decomposed Low-Rank Adaptation

Self-Rewarding Language Models

Orion-14B: Open-source Multilingual Large Language Models

MambaByte: Token-free Selective State Space Model

MM-LLMs: Recent Advances in MultiModal Large Language Models