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Showing 276300 of 1596 papers

TitleStatusHype
M1: Towards Scalable Test-Time Compute with Mamba Reasoning ModelsCode1
MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language ModelsCode1
LoRA Soups: Merging LoRAs for Practical Skill Composition TasksCode1
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and LayersCode1
LogQuant: Log-Distributed 2-Bit Quantization of KV Cache with Superior Accuracy PreservationCode1
Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMsCode1
LLMThinkBench: Towards Basic Math Reasoning and Overthinking in Large Language ModelsCode1
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant EvaluationCode1
Advancing Multimodal Reasoning via Reinforcement Learning with Cold StartCode1
LEVER: Learning to Verify Language-to-Code Generation with ExecutionCode1
OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization ModelingCode1
CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language ModelsCode1
Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPTCode1
MathChat: Converse to Tackle Challenging Math Problems with LLM AgentsCode1
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation ExtractionCode1
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree SearchCode1
Learning Goal-Conditioned Representations for Language Reward ModelsCode1
Learning Multi-Step Reasoning by Solving Arithmetic TasksCode1
Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning CapabilityCode1
An Early Evaluation of GPT-4V(ision)Code1
Let's Verify Math Questions Step by StepCode1
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
Large Language Models Can Be Easily Distracted by Irrelevant ContextCode1
Large (Vision) Language Models are Unsupervised In-Context LearnersCode1
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