25 citations · 118 across the 22 of their papers we have counts for
8 papers · 1 filter
Training Software Engineering Agents and Verifiers with SWE-Gym
Jiayi Pan, Xingyao Wang, Graham Neubig +4
We present SWE-Gym, the first environment for training real-world software engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python task instances, each comprising a code…
OpenHands: An Open Platform for AI Software Developers as Generalist Agents
Xingyao Wang, Boxuan Li, Yufan Song +21
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways. At the same…
SOLO: A Single Transformer for Scalable Vision-Language Modeling
Yangyi Chen, Xingyao Wang, Hao Peng +1
We present SOLO, a single transformer for Scalable visiOn-Language mOdeling. Current large vision-language models (LVLMs) such as LLaVA mostly employ heterogeneous architectures th…
SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales
Tianyang Xu, Shujin Wu, Shizhe Diao +4
Large language models (LLMs) often generate inaccurate or fabricated information and generally fail to indicate their confidence, which limits their broader applications. Previous…
Advancing LLM Reasoning Generalists with Preference Trees
Lifan Yuan, Ganqu Cui, Hanbin Wang +12
We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning. Finetuned from Mistral-7B and CodeLlama-70B, Eurus models achieve state-of-the-art results amon…
Visually Descriptive Language Model for Vector Graphics Reasoning
Zhenhailong Wang, Joy Hsu, Xingyao Wang +4
Despite significant advancements, large multimodal models (LMMs) still struggle to bridge the gap between low-level visual perception -- focusing on shapes, sizes, and layouts -- a…
Executable Code Actions Elicit Better LLM Agents
Xingyao Wang, Yangyi Chen, Lifan Yuan +4
Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking tools and controlling robots, show great potential in tackling real-world challe…
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Ke Yang, Jiateng Liu, John Wu +9
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural langua…