6 papers
Steer, Don't Solve: Training Small Critic Models for Large Code Agents
Shubham Gandhi, Yiqing Xie, Atharva Naik +2
End-to-end code agent training is resource-intensive and plateaus on the strategy-level reasoning needed to resolve code issues, since jointly optimizing code-level execution and s…
Hybrid-Gym: Training Coding Agents to Generalize Across Tasks
Yiqing Xie, Emmy Liu, Gaokai Zhang +7
When assessing the quality of coding agents, predominant benchmarks focus on solving single issues on GitHub, such as SWE-Bench. In contrast, in real use, these agents solve more v…
When Agents go Astray: Course-Correcting SWE Agents with PRMs
Shubham Gandhi, Jason Tsay, Jatin Ganhotra +2
Large Language Model (LLM) agents are increasingly deployed for complex, multi-step software engineering (SWE) tasks. However, their trajectories often contain costly inefficiencie…
An Empirical Study on Strong-Weak Model Collaboration for Repo-level Code Generation
Shubham Gandhi, Atharva Naik, Yiqing Xie +1
We study cost-efficient collaboration between strong and weak language models for repository-level code generation, where the weak model handles simpler tasks at lower cost, and th…
ResearchCodeAgent: An LLM Multi-Agent System for Automated Codification of Research Methodologies
Shubham Gandhi, Dhruv Shah, Manasi Patwardhan +2
In this paper we introduce ResearchCodeAgent, a novel multi-agent system leveraging large language models (LLMs) agents to automate the codification of research methodologies descr…
BudgetMLAgent: A Cost-Effective LLM Multi-Agent system for Automating Machine Learning Tasks
Shubham Gandhi, Manasi Patwardhan, Lovekesh Vig +1
Large Language Models (LLMs) excel in diverse applications including generation of code snippets, but often struggle with generating code for complex Machine Learning (ML) tasks. A…