DecompRL is an RL method that learns modular code decomposition for LLMs, enabling exponential candidate generation via recombination to solve harder coding problems with lower GPU cost.
Parsel: A (de-) compositional framework for algorithmic reasoning with language models
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SWE-agent introduces a custom agent-computer interface that lets LM agents solve software engineering tasks, reaching 12.5% pass@1 on SWE-bench and 87.7% on HumanEvalFix, exceeding prior non-interactive approaches.
LiveCodeBench collects 400 recent contest problems to create a contamination-free benchmark evaluating LLMs on code generation and related capabilities like self-repair and execution.
citing papers explorer
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DecompRL: Solving Harder Problems by Learning Modular Code Generation
DecompRL is an RL method that learns modular code decomposition for LLMs, enabling exponential candidate generation via recombination to solve harder coding problems with lower GPU cost.
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SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
SWE-agent introduces a custom agent-computer interface that lets LM agents solve software engineering tasks, reaching 12.5% pass@1 on SWE-bench and 87.7% on HumanEvalFix, exceeding prior non-interactive approaches.
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LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
LiveCodeBench collects 400 recent contest problems to create a contamination-free benchmark evaluating LLMs on code generation and related capabilities like self-repair and execution.