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#Code Generation

82개의 포스트

[논문리뷰] Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines

본 논문은 Four-Condition Design을 통해 성능 이득을 Additive하게 분해하는 프레임워크를 제안합니다. 이 방법론은 Generator 기반 성능($x_1$), 표준 Revision($x_2$), 독립 재해결 제어($x_3$), 구조화된 Null 초안 제어($x_4$)를 비교하여 세 가지 효과를 각각 산출합니다.
#Review#Multi-LLM Pipeline#Iterative Refinement#Self-Correction#Task-Time Scaling#Code Generation#MCQ
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[논문리뷰] MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants

Yuante Li이 arXiv에 게시한 'MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants' 논문에 대한 자세한 리뷰입니다.
#Review#Large Language Models (LLMs)#Code Generation#HTML#Interactive Applications#Benchmark#MINIAPPBENCH#Agentic Evaluation#MINIAPPEVAL#Real-World Principles#Human-AI Interaction
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[논문리뷰] BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation

Xiaohua Wang이 arXiv에 게시한 'BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation' 논문에 대한 자세한 리뷰입니다.
#Review#Self-Supervised Learning#Code Generation#Documentation Generation#Back-Translation#Reinforcement Learning#Large Language Models (LLMs)#Code-Documentation Alignment#Low-Resource Languages
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[논문리뷰] SWE-Bench++: A Framework for the Scalable Generation of Software Engineering Benchmarks from Open-Source Repositories

arXiv에 게시된 'SWE-Bench++: A Framework for the Scalable Generation of Software Engineering Benchmarks from Open-Source Repositories' 논문에 대한 자세한 리뷰입니다.
#Review#Software Engineering Benchmarks#Large Language Models (LLMs)#Code Generation#Automated Benchmark Generation#Multilingual#GitHub Pull Requests#Test Oracle#Fine-tuning
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[논문리뷰] Tiny Model, Big Logic: Diversity-Driven Optimization Elicits Large-Model Reasoning Ability in VibeThinker-1.5B

arXiv에 게시된 'Tiny Model, Big Logic: Diversity-Driven Optimization Elicits Large-Model Reasoning Ability in VibeThinker-1.5B' 논문에 대한 자세한 리뷰입니다.
#Review#Small Language Models#Reasoning#Diversity Optimization#Supervised Fine-Tuning (SFT)#Reinforcement Learning (RL)#Spectrum-to-Signal Principle (SSP)#Mathematical Reasoning#Code Generation
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[논문리뷰] DRIVE: Data Curation Best Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation

arXiv에 게시된 'DRIVE: Data Curation Best Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation' 논문에 대한 자세한 리뷰입니다.
#Review#Reinforcement Learning with Verifiable Reward#Competitive Programming#Code Generation#Data Curation#Curriculum Learning#Supervised Fine-tuning#Entropy Expansion
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