Leveraging training and search for better software engineering agents

At Nebius, the focus is on advancing large language model (LLM)-based agentic systems for automated software engineering, which have matured to handle routine tasks and are evolving to tackle more complex challenges. These systems, unlike traditional coding assistants, execute commands, write and test code, and refine their actions autonomously, significantly boosting efficiency and productivity. The SWE-bench benchmark is pivotal in evaluating these agents, emphasizing the generation of patches from issue descriptions to pass relevant tests within containerized environments. While current efforts often emphasize sophisticated frameworks for agentic actions, the "bitter lesson" suggests that scalable methods like search and learning outperform these structures in the long run. Top-performing agents leverage frontier models such as GPT-4o, excelling in target domains due to extensive resource investment. An alternative approach involves guided search with critic models, enhancing solution accuracy by steering action generation. Nebius explores techniques like 1-step lookahead and trajectory selection using critic models, yielding significant performance improvements and narrowing the gap between average and best-of-N performance. These insights suggest that sophisticated search strategies, when combined with learning, hold promise for the future of automated software engineering, urging further exploration into scalable approaches and advanced search methods to enhance agentic systems' reliability and adaptability.

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