debug-gym: A Text-Based Environment for Interactive Debugging

• URL: http://arxiv.org/abs/2503.21557v1
• Date: Thu, 27 Mar 2025 14:43:28 GMT
• Title: debug-gym: A Text-Based Environment for Interactive Debugging
• Authors: Xingdi Yuan, Morgane M Moss, Charbel El Feghali, Chinmay Singh, Darya Moldavskaya, Drew MacPhee, Lucas Caccia, Matheus Pereira, Minseon Kim, Alessandro Sordoni, Marc-Alexandre Côté,
• Abstract summary: Large Language Models (LLMs) are increasingly relied upon for coding tasks.<n>We posit that LLMs can benefit from the ability to interactively explore a to gather the information relevant to their task.<n>We present a textual environment, namely debug-gym, for developing LLM-based agents in an interactive coding setting.
• Score: 55.11603087371956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly relied upon for coding tasks, yet in most scenarios it is assumed that all relevant information can be either accessed in context or matches their training data. We posit that LLMs can benefit from the ability to interactively explore a codebase to gather the information relevant to their task. To achieve this, we present a textual environment, namely debug-gym, for developing LLM-based agents in an interactive coding setting. Our environment is lightweight and provides a preset of useful tools, such as a Python debugger (pdb), designed to facilitate an LLM-based agent's interactive debugging. Beyond coding and debugging tasks, this approach can be generalized to other tasks that would benefit from information-seeking behavior by an LLM agent.

Related papers

• Codellm-Devkit: A Framework for Contextualizing Code LLMs with Program Analysis Insights [9.414198519543564]
  We present codellm-devkit (hereafter, CLDK'), an open-source library that significantly simplifies the process of performing program analysis. CLDK offers developers an intuitive and user-friendly interface, making it incredibly easy to provide rich program analysis context to code LLMs.
  arXiv  Detail & Related papers  (2024-10-16T20:05:59Z)
• Bug In the Code Stack: Can LLMs Find Bugs in Large Python Code Stacks [1.3586572110652484]
  This study explores the capabilities of Large Language Models (LLMs) in retrieving contextual information from large text documents. Our benchmark, Bug In The Code Stack (BICS), is designed to assess the ability of LLMs to identify simple syntax bugs within large source code. Our findings reveal three key insights: (1) code-based environments pose significantly more challenge compared to text-based environments for retrieval tasks, (2) there is a substantial performance disparity among different models, and (3) there is a notable correlation between longer context lengths and performance degradation.
  arXiv  Detail & Related papers  (2024-06-21T17:37:10Z)
• Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents [56.822238860147024]
  Augmenting large language models with external tools has emerged as a promising approach to extend their utility. Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning. We propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• AGILE: A Novel Reinforcement Learning Framework of LLM Agents [7.982249117182315]
  We introduce a novel reinforcement learning framework of LLM agents designed to perform complex conversational tasks with users. The agent possesses capabilities beyond conversation, including reflection, tool usage, and expert consultation. Our experiments show that AGILE agents based on 7B and 13B LLMs trained with PPO can outperform GPT-4 agents.
  arXiv  Detail & Related papers  (2024-05-23T16:17:44Z)
• Middleware for LLMs: Tools Are Instrumental for Language Agents in Complex Environments [35.670330583914904]
  Large language models (LLMs) are envisioned as generalist agents capable of operating within complex environments. We introduce a novel class of tools to augment LLMs in handling such complexity. In two representative complex environments -- knowledge bases (KBs) and databases -- we demonstrate the significant potential of augmenting language agents with tools.
  arXiv  Detail & Related papers  (2024-02-22T16:18:07Z)
• DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models (Exemplified as A Video Agent) [73.10899129264375]
  This paper explores DoraemonGPT, a comprehensive and conceptually elegant system driven by LLMs to understand dynamic scenes.<n>Given a video with a question/task, DoraemonGPT begins by converting the input video into a symbolic memory that stores task-related attributes.<n>We extensively evaluate DoraemonGPT's effectiveness on three benchmarks and several in-the-wild scenarios.
  arXiv  Detail & Related papers  (2024-01-16T14:33:09Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach [31.6589518077397]
  Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets. LLMs can assist an embodied agent in solving complex sequential decision making tasks by providing high-level instructions. We propose When2Ask, a reinforcement learning based approach that learns when it is necessary to query LLMs for high-level instructions.
  arXiv  Detail & Related papers  (2023-06-06T11:49:09Z)
• Low-code LLM: Graphical User Interface over Large Language Models [115.08718239772107]
  This paper introduces a novel human-LLM interaction framework, Low-code LLM. It incorporates six types of simple low-code visual programming interactions to achieve more controllable and stable responses. We highlight three advantages of the low-code LLM: user-friendly interaction, controllable generation, and wide applicability.
  arXiv  Detail & Related papers  (2023-04-17T09:27:40Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.