Papers Math
“Math” 태그가 달린 논문 1,596편 · 필터 해제
OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling
Different base language model families, such as Llama and Qwen, exhibit divergent behaviors during post-training with reinforcement learning (RL), especially on reasoning-intensive tasks. What makes a base language model…
Language ModelingLanguage ModellingMathreinforcement-learning+2When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs
Recent advancements in large language models (LLMs) have shifted focus toward scaling inference-time compute, improving performance without retraining the model. A common approach is to sample multiple outputs in paralle…
MathAALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control
Large reasoning models (LRMs) achieve impressive reasoning capabilities by generating lengthy chain-of-thoughts, but this "overthinking" incurs high latency and cost without commensurate accuracy gains. In this work, we …
Language ModelingLanguage ModellingLarge Language ModelMathMulti-lingual Functional Evaluation for Large Language Models
Multi-lingual competence in large language models is often evaluated via static data benchmarks such as Belebele, M-MMLU and M-GSM. However, these evaluations often fail to provide an adequate understanding of the practi…
BelebeleInstruction FollowingMathMMLUConfucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning
We introduce Confucius3-Math, an open-source large language model with 14B parameters that (1) runs efficiently on a single consumer-grade GPU; (2) achieves SOTA performances on a range of mathematical reasoning tasks, o…
GPULarge Language ModelMathMathematical Reasoning+2ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs
Process Reward Models (PRMs) have recently emerged as a powerful framework for supervising intermediate reasoning steps in large language models (LLMs). Previous PRMs are primarily trained on model final output responses…
MathPlan for Speed -- Dilated Scheduling for Masked Diffusion Language Models
Masked diffusion language models (MDLM) have shown strong promise for non-autoregressive text generation, yet existing samplers act as implicit planners, selecting tokens to unmask via denoiser confidence or entropy scor…
Code CompletionGSM8KHumanEvalMath+3Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
Educational disparities are rooted in and perpetuate social inequalities across multiple dimensions such as race, socioeconomic status, and geography. To reduce disparities, most intervention strategies focus on a single…
MathShrinking the Generation-Verification Gap with Weak Verifiers
Verifiers can improve language model capabilities by scoring and ranking responses from generated candidates. Currently, high-quality verifiers are either unscalable (e.g., humans) or limited in utility (e.g., tools like…
MathEvolving Prompts In-Context: An Open-ended, Self-replicating Perspective
We propose a novel prompt design paradigm that challenges conventional wisdom in large language model (LLM) prompting. While conventional wisdom prioritizes well-crafted instructions and demonstrations for in-context lea…
In-Context LearningLarge Language ModelMathQuestion AnsweringNo Free Lunch: Rethinking Internal Feedback for LLM Reasoning
Reinforcement learning has emerged as a powerful paradigm for post-training large language models (LLMs) to improve reasoning. Approaches like Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning …
Mathreinforcement-learningReinforcement LearningLeveraging LLMs to Assess Tutor Moves in Real-Life Dialogues: A Feasibility Study
Tutoring improves student achievement, but identifying and studying what tutoring actions are most associated with student learning at scale based on audio transcriptions is an open research problem. This present study i…
MathOJBench: A Competition Level Code Benchmark For Large Language Models
Recent advancements in large language models (LLMs) have demonstrated significant progress in math and code reasoning capabilities. However, existing code benchmark are limited in their ability to evaluate the full spect…
MathAgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need
Large language model based multi-agent systems have demonstrated significant potential in social simulation and complex task resolution domains. However, current frameworks face critical challenges in system architecture…
GSM8KHumanEvalLarge Language ModelMath+1SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks
Large Language Models (LLMs) are experiencing rapid advancements in complex reasoning, exhibiting remarkable generalization in mathematics and programming. In contrast, while spatial intelligence is fundamental for Visio…
MathSpatial ReasoningEssential-Web v1.0: 24T tokens of organized web data
Data plays the most prominent role in how language models acquire skills and knowledge. The lack of massive, well-organized pre-training datasets results in costly and inaccessible data pipelines. We present Essential-We…
MathXolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team
Despite impressive progress on complex reasoning, current large language models (LLMs) typically operate in isolation - treating each problem as an independent attempt, without accumulating or integrating experiential kn…
Code GenerationGSM8KMathMathematical ReasoningUtility-Driven Speculative Decoding for Mixture-of-Experts
GPU memory bandwidth is the main bottleneck for low-latency Large Language Model (LLM) inference. Speculative decoding leverages idle GPU compute by using a lightweight drafter to propose K tokens, which the LLM verifies…
GPULarge Language ModelMathMixture-of-ExpertsWeakest Link in the Chain: Security Vulnerabilities in Advanced Reasoning Models
The introduction of advanced reasoning capabilities have improved the problem-solving performance of large language models, particularly on math and coding benchmarks. However, it remains unclear whether these reasoning …
MathAdaptive Guidance Accelerates Reinforcement Learning of Reasoning Models
We study the process through which reasoning models trained with reinforcement learning on verifiable rewards (RLVR) can learn to solve new problems. We find that RLVR drives performance in two main ways: (1) by compress…
Mathreinforcement-learningReinforcement Learning