这篇论文提出一个叫AutoTrainess的智能体，能自己跑模型训练流程，不用人盯着。在PostTrainBench上比纯命令行强，还能帮DeepSeek-V4涨分。
AutoTrainess是一个基于大语言模型的智能体，通过暴露规划、数据准备、训练、评估和日志等操作作为代理-计算机接口，自动化语言模型的后训练流程。在PostTrainBench基准上，AutoTrainess搭配GPT-5.4（Codex）取得平均26.94分，超过CLI-only基线的23.21分。它还能跨模型泛化，将DeepSeek-V4-Flash（OpenCode）的得分从12.13提升至19.58。该研究提出将人工经验外部化为显式工作流和约束，引导智能体更有效且可靠地执行训练任务。
AutoTrainess: Teaching Language Models to Improve Language Models Autonomously
Training language models (LMs) remains a highly human-intensive process, even as frontier language model agents become increasingly capable at software engineering and other long-horizon tasks. A central challenge is that autonomous post-training is not just a coding problem: it requires the agent to repeatedly plan iterations, construct benchmark-aligned data, run stable training jobs, evaluate checkpoints, and preserve experiment state across many hours of interaction. We present AutoTrainess, a LM agent that exposes these operations as a repository of agent-computer interfaces for planning, data preparation, training, evaluation, and logging. Rather than leaving the agent to operate in a raw CLI environment with an underspecified action space, AutoTrainess externalizes prior human experience as explicit workflows, rules, and execution constraints that guide the agent toward effective and reliable training behavior. On PostTrainBench, AutoTrainess consistently outperforms CLI-only baselines, achieving 26.94 average score with GPT-5.4 (Codex) versus 23.21 for CLI-only. It also generalizes across models and harnesses, improving DeepSeek-V4-Flash (OpenCode) from 12.13 to 19.58.