Papers Math
“Math” 태그가 달린 논문 1,596편 · 필터 해제
VAR-MATH: Probing True Mathematical Reasoning in Large Language Models via Symbolic Multi-Instance Benchmarks
Recent advances in reinforcement learning (RL) have led to substantial improvements in the mathematical reasoning abilities of large language models (LLMs), as measured by standard benchmarks. However, these gains often …
MathMathematical ReasoningReinforcement Learning (RL)QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
Reinforcement learning (RL) has become a key component in training large language reasoning models (LLMs). However, recent studies questions its effectiveness in improving multi-step reasoning-particularly on hard proble…
MathReinforcement Learning (RL)Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training
Recent advancements in reasoning-focused language models such as OpenAI's O1 and DeepSeek-R1 have shown that scaling test-time computation-through chain-of-thought reasoning and iterative exploration-can yield substantia…
Code GenerationMathreinforcement-learningReinforcement Learning+2Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Coding
Large Language Models (LLMs) enable new possibilities for qualitative research at scale, including coding and data annotation. While multi-agent systems (MAS) can emulate human coding workflows, their benefits over singl…
MathPersonalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing
We introduce ExRec, a general framework for personalized exercise recommendation with semantically-grounded knowledge tracing. Our method builds on the observation that existing exercise recommendation approaches simulat…
Knowledge TracingMathQ-LearningReinforcement Learning (RL)Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
The reasoning capabilities of large language models (LLMs) have been a longstanding focus of research. Recent works have further enhanced these capabilities using reinforcement learning (RL), with many new methods claimi…
MathMathematical ReasoningMemorizationReinforcement Learning (RL)A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning
Enhancing the mathematical reasoning of Large Language Models (LLMs) is a pivotal challenge in advancing AI capabilities. While Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) are the dominant training parad…
MathMathematical ReasoningReinforcement Learning (RL)Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs
Can a pretrained neural network adapt its architecture to different inputs without any finetuning? Do we need all layers for simple tasks, and are they adequate for challenging tasks? We found that the layers of a pretra…
CoLALarge Language ModelMathSqueeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model
Reinforcement Learning (RL) has demonstrated its potential to improve the reasoning ability of Large Language Models (LLMs). One major limitation of most existing Reinforcement Finetuning (RFT) methods is that they are o…
Language ModelingLanguage ModellingLarge Language ModelMath+1The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains
Improvements in language models are often driven by improving the quality of the data we train them on, which can be limiting when strong supervision is scarce. In this work, we show that paired preference data consistin…
MathMMLUCoRE: Enhancing Metacognition with Label-free Self-evaluation in LRMs
Large reasoning models (LRMs) have demonstrated impressive capabilities in domains like mathematics and program synthesis. Despite their strong performance, LRMs often exhibit overthinking -- excessive and redundant reas…
GSM8KMathMathematical ReasoningProgram SynthesisActivation Steering for Chain-of-Thought Compression
Large language models (LLMs) excel at complex reasoning when they include intermediate steps, known as "chains of thought" (CoTs). However, these rationales are often overly verbose, even for simple problems, leading to …
GSM8KMathLLMThinkBench: Towards Basic Math Reasoning and Overthinking in Large Language Models
Large Language Models (LLMs) have achieved remarkable performance on complex mathematical benchmarks, yet often struggle with simple arithmetic tasks and exhibit a tendency toward over-explaining or "overthinking" answer…
BenchmarkingGPUMathEffects of structure on reasoning in instance-level Self-Discover
The drive for predictable LLM reasoning in their integration with compound systems has popularized structured outputs, yet concerns remain about performance trade-offs compared to unconstrained natural language. At the s…
MathEvoAgentX: An Automated Framework for Evolving Agentic Workflows
Multi-agent systems (MAS) have emerged as a powerful paradigm for orchestrating large language models (LLMs) and specialized tools to collaboratively address complex tasks. However, existing MAS frameworks often require …
Code GenerationMathMathematical Problem-SolvingmbppEnergy-Based Transformers are Scalable Learners and Thinkers
Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer from several limitations: they are moda…
DenoisingImage DenoisingMathSPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning
Recent advances in reinforcement learning have shown that language models can develop sophisticated reasoning through training on tasks with verifiable rewards, but these approaches depend on human-curated problem-answer…
MathMulti-agent Reinforcement LearningDo Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model
Large Reasoning Models (LRMs) excel at solving complex problems but face an overthinking dilemma. When handling simple tasks, they often produce verbose responses overloaded with thinking tokens (e.g., wait, however). Th…
MathBridging Offline and Online Reinforcement Learning for LLMs
We investigate the effectiveness of reinforcement learning methods for finetuning large language models when transitioning from offline to semi-online to fully online regimes for both verifiable and non-verifiable tasks.…
Instruction FollowingMathreinforcement-learningReinforcement LearningWhere to find Grokking in LLM Pretraining? Monitor Memorization-to-Generalization without Test
Grokking, i.e., test performance keeps improving long after training loss converged, has been recently witnessed in neural network training, making the mechanism of generalization and other emerging capabilities such as …
Code GenerationLarge Language ModelMathMemorization