Papers Math
“Math” 태그가 달린 논문 1,596편 · 필터 해제
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework
High-quality math datasets are crucial for advancing the reasoning abilities of large language models (LLMs). However, existing datasets often suffer from three key issues: outdated and insufficient challenging content, …
MathThe Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for training language models (LMs) on reasoning tasks that elicit emergent long chains of thought (CoTs). Unlike supervised learning, it updat…
MathMathematical ReasoningSynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis
Vision-language models (VLMs) trained via reinforcement learning with verifiable reward (RLVR) have shown notable progress in scaling test-time compute effectively. In this work, we investigate how synthesized RL data ca…
8kMathVisual ReasoningInvariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning
Machine unlearning offers a promising solution to privacy and safety concerns in large language models (LLMs) by selectively removing targeted knowledge while preserving utility. However, current methods are highly sensi…
Machine UnlearningMathSentiment AnalysisKnowledge or Reasoning? A Close Look at How LLMs Think Across Domains
Recent advances in reasoning-enhanced Large Language Models such as OpenAI-o1/3 and DeepSeek-R1 have significantly improved performance on complex tasks. However, the quality and transparency of their internal reasoning …
MathReinforcement Learning (RL)GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking
Despite notable advancements in multimodal reasoning, leading Multimodal Large Language Models (MLLMs) still underperform on vision-centric multimodal reasoning tasks in general scenarios. This shortfall stems from their…
4kMathMathematical ReasoningMultimodal ReasoningAReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning
Reinforcement learning (RL) has become a dominant paradigm for training large language models (LLMs), particularly for reasoning tasks. Effective RL for LLMs requires massive parallelization and poses an urgent need for …
GPUMathReinforcement Learning (RL)Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning
Recent advances in model distillation demonstrate that data from advanced reasoning models (e.g., DeepSeek-R1, OpenAI's o1) can effectively transfer complex reasoning abilities to smaller, efficient student models. Howev…
MathMathematical ReasoningAccelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking
Recent masked diffusion models (MDMs) have shown competitive performance compared to autoregressive models (ARMs) for language modeling. While most literature has focused on performance enhancing sampling procedures, eff…
Language ModelingLanguage ModellingMathMixed-R1: Unified Reward Perspective For Reasoning Capability in Multimodal Large Language Models
Recent works on large language models (LLMs) have successfully demonstrated the emergence of reasoning capabilities via reinforcement learning (RL). Although recent efforts leverage group relative policy optimization (GR…
MathMultiple-choiceReinforcement Learning (RL)A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings
Large Reasoning Models (LRMs) achieve superior performance by extending the thought length. However, a lengthy thinking trajectory leads to reduced efficiency. Most of the existing methods are stuck in the assumption of …
MathReflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
We explore a method for improving the performance of large language models through self-reflection and reinforcement learning. By incentivizing the model to generate better self-reflections when it answers incorrectly, w…
Mathreinforcement-learningReinforcement LearningAgent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks
Deep reasoning is fundamental for solving complex tasks, especially in vision-centric scenarios that demand sequential, multimodal understanding. However, existing benchmarks typically evaluate agents with fully syntheti…
Autonomous DrivingMathMultimodal ReasoningVisual ReasoningSiLVR: A Simple Language-based Video Reasoning Framework
Recent advances in test-time optimization have led to remarkable reasoning capabilities in Large Language Models (LLMs), enabling them to solve highly complex problems in math and coding. However, the reasoning capabilit…
MathMMEMMLUToken Reduction+2DINGO: Constrained Inference for Diffusion LLMs
Diffusion LLMs have emerged as a promising alternative to conventional autoregressive LLMs, offering significant potential for improved runtime efficiency. However, existing diffusion models lack the ability to provably …
MathLLM Performance for Code Generation on Noisy Tasks
This paper investigates the ability of large language models (LLMs) to recognise and solve tasks which have been obfuscated beyond recognition. Focusing on competitive programming and benchmark tasks (LeetCode and MATH),…
BenchmarkingCode GenerationMathMatryoshka Model Learning for Improved Elastic Student Models
Industry-grade ML models are carefully designed to meet rapidly evolving serving constraints, which requires significant resources for model development. In this paper, we propose MatTA, a framework for training multiple…
LAMBADAMathmodelPBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics
Recently, long chain of thought (LCoT), Large Language Models (LLMs), have taken the machine learning world by storm with their breathtaking reasoning capabilities. However, are the abstract reasoning abilities of these …
MathDiscriminative Policy Optimization for Token-Level Reward Models
Process reward models (PRMs) provide more nuanced supervision compared to outcome reward models (ORMs) for optimizing policy models, positioning them as a promising approach to enhancing the capabilities of LLMs in compl…
GSM8KLanguage ModelingLanguage ModellingMath+2Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation
Final-answer-based metrics are commonly used for evaluating large language models (LLMs) on math word problems, often taken as proxies for reasoning ability. However, such metrics conflate two distinct sub-skills: abstra…
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