Скачать или смотреть Kimi-Dev: Agentless Training as Skill Prior for SWE-Agents (Sep 2025)

Title: Kimi-Dev: Agentless Training as Skill Prior for SWE-Agents (Sep 2025)
Link: http://arxiv.org/abs/2509.23045v2
Date: September 2025

Summary:
This paper introduces Kimi-Dev, an open-source Large Language Model (LLM) for Software Engineering (SWE) tasks. It challenges the traditional dichotomy between multi-turn SWE-Agent frameworks and workflow-based Agentless methods, arguing that Agentless training can induce crucial skill priors—such as localization, code editing, and self-reflection—that enhance the efficiency and effectiveness of SWE-Agent adaptation. Kimi-Dev achieves state-of-the-art performance of 60.4% on SWE-bench Verified among workflow-based solutions. With an additional SFT adaptation on 5k publicly available trajectories, Kimi-Dev powers SWE-Agents to 48.6% pass@1, on par with leading proprietary models. The results demonstrate that structured skill priors from Agentless training bridge workflow and agentic frameworks for transferable coding agents.

Key Topics:
Software Engineering LLMs
Agentless Training
SWE-Agents
Skill Prior Transfer
Code Generation and Repair

Chapters:
00:00 - Agent vs. Agentless Dichotomy
00:57 - Kimi-Dev: Skill Prior Results
02:29 - Agent Challenges & Workflows
04:27 - Agentless as Skill Prior
04:59 - Kimi-Dev Mid-Training Stage
06:05 - SFT and RL Training
07:05 - Deep Dive into RL Training
08:32 - Test-Time Self-Play
09:44 - Data-Efficient Agent Adaptation
11:13 - Measuring Improved Agent Skills
13:08 - Generalization & Emergent Abilities
15:11 - Conclusion & Future Directions

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