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arXiv · 2503.18494

Verbal Process Supervision Elicits Better Coding Agents

Hao-Yuan Chen·Cheng-Pong Huang·Jui-Ming Yao
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Abstract

The emergence of large language models and their applications as AI agents have significantly advanced state-of-the-art code generation benchmarks, transforming modern software engineering tasks. However, even with test-time computed reasoning models, these systems still struggle with complex software engineering challenges. This work introduces CURA, a code understanding and reasoning agent system enhanced with verbal process supervision (VPS), achieving a 3.65\% improvement over baseline models on challenging benchmarks like BigCodeBench. Furthermore, CURA, when paired with the o3-mini model and VPS techniques, attains state-of-the-art performance. This work represents a step forward in integrating reasoning-driven architectures with LLM-based code generation, enabling agentic reasoning for language models to solve complex software engineering tasks.

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Hao-Yuan Chen, Cheng-Pong Huang, Jui-Ming Yao. 2025-03-24. Verbal Process Supervision Elicits Better Coding Agents. https://arxiv.org/abs/2503.18494

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