104 citations · 203 across the 12 of their papers we have counts for
14 papers
Humans are Missing from AI Coding Agent Research
Zora Z. Wang, John Yang, Kilian Lieret +10
Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebase…
Toward Training Superintelligent Software Agents through Self-Play SWE-RL
Yuxiang Wei, Zhiqing Sun, Emily McMilin +6
While current software agents powered by large language models (LLMs) and agentic reinforcement learning (RL) can boost programmer productivity, their training data (e.g., GitHub i…
Live-SWE-agent: Can Software Engineering Agents Self-Evolve on the Fly?
Chunqiu Steven Xia, Zhe Wang, Yan Yang +2
Large Language Models (LLMs) are reshaping almost all industries, including software engineering. In recent years, a number of LLM agents have been proposed to solve real-world sof…
CWM: An Open-Weights LLM for Research on Code Generation with World Models
FAIR CodeGen team, Jade Copet, Quentin Carbonneaux +48
We release Code World Model (CWM), a 32-billion-parameter open-weights LLM, to advance research on code generation with world models. To improve code understanding beyond what can…
PurpCode: Reasoning for Safer Code Generation
Jiawei Liu, Nirav Diwan, Zhe Wang +11
We introduce PurpCode, the first post-training recipe for training safe code reasoning models towards generating secure code and defending against malicious cyberactivities. PurpCo…
SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution
Yuxiang Wei, Olivier Duchenne, Jade Copet +6
The recent DeepSeek-R1 release has demonstrated the immense potential of reinforcement learning (RL) in enhancing the general reasoning capabilities of large language models (LLMs)…
SelfCodeAlign: Self-Alignment for Code Generation
Yuxiang Wei, Federico Cassano, Jiawei Liu +7
Instruction tuning is a supervised fine-tuning approach that significantly improves the ability of large language models (LLMs) to follow human instructions. We propose SelfCodeAli…
Evaluating Language Models for Efficient Code Generation
Jiawei Liu, Songrun Xie, Junhao Wang +3
We introduce Differential Performance Evaluation (DPE), a framework designed to reliably evaluate Large Language Models (LLMs) for efficient code generation. Traditional coding ben…
RepoQA: Evaluating Long Context Code Understanding
Jiawei Liu, Jia Le Tian, Vijay Daita +5
Recent advances have been improving the context windows of Large Language Models (LLMs). To quantify the real long-context capabilities of LLMs, evaluators such as the popular Need…
XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts
Yifeng Ding, Jiawei Liu, Yuxiang Wei +2
We introduce XFT, a simple yet powerful training scheme, by simply merging upcycled Mixture-of-Experts (MoE) to unleash the performance limit of instruction-tuned code Large Langua…
StarCoder 2 and The Stack v2: The Next Generation
Anton Lozhkov, Raymond Li, Loubna Ben Allal +63
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership wi…
Efficient Title Reranker for Fast and Improved Knowledge-Intense NLP
Ziyi Chen, Jize Jiang, Daqian Zuo +3
In recent RAG approaches, rerankers play a pivotal role in refining retrieval accuracy with the ability of revealing logical relations for each pair of query and text. However, exi…
Magicoder: Empowering Code Generation with OSS-Instruct
Yuxiang Wei, Zhe Wang, Jiawei Liu +2
We introduce Magicoder, a series of fully open-source (code, weights, and data) Large Language Models (LLMs) for code that significantly closes the gap with top code models while h…
Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair
Yuxiang Wei, Chunqiu Steven Xia, Lingming Zhang
During Automated Program Repair (APR), it can be challenging to synthesize correct patches for real-world systems in general-purpose programming languages. Recent Large Language Mo…