为智能体后训练预调基础模型：一份开放的中间训练方案
Priming Base Models for Agentic Post-Training: An Open Midtraining Recipe
AI总结：
本研究提出为智能体后训练预调基础模型的开放中间训练方案，在Nemotron系列模型上验证其可提升SWE相关基准性能与鲁棒性，明确了中间训练的关键设置与评估方式。
摘要：
前沿模型，无论是通用型还是领域专用型，如今都在预训练和后训练之间加入了中间训练阶段，但关于该阶段如何构建的公开研究成果却很少。我们提出了一份开放的中间训练方案，用于为智能体后训练预调基础模型，并在两个不同规模的Nemotron模型上验证了其有效性。预训练通常在短上下文下运行，几乎没有智能体交互；而监督微调（SFT）数据大多是合成的，过长的SFT阶段会损害模型后续的强化学习（RL）可训练性。我们的中间训练阶段介于两者之间：它在256K的上下文窗口下，用数千亿个token进行训练，训练数据是多样化的混合数据，包括打包的代码仓库、拉取请求、提交历史、构建日志等自然软件工程数据，以及一小部分智能体轨迹。在Nemotron 3.5 Lightning模型上，经过相同的完整SFT后，500B的中间训练token将SWE-bench Verified的性能从60.7提升至64.5，SWE-bench Multilingual从55.8提升至62.8，SWE-bench Pro从32.0提升至35.5；在更大的Nemotron 3.5 Super模型上，经过相同的完整SFT后，280B的中间训练token将更具挑战性的长周期任务基准DeepSWE的性能从11.7提升至15.7，SWE-bench Verified从72.9提升至74.3。中间训练还提升了两个模型在不同智能体测试框架下的鲁棒性。我们报告了该方案背后的消融实验，以及基于短固定SFT运行的评估协议，因为基础模型的代理指标与后训练结果的相关性并不可靠。
英文摘要：
Frontier models, general-purpose and domain-specific alike, now include a midtraining stage between pretraining and post-training, but little has been published about how this stage is built. We present an open midtraining recipe that primes base models for agentic post-training, and we show its effectiveness on two Nemotron models of different sizes. Pretraining typically runs at short context and sees almost no agent interactions, while SFT data is mostly synthetic, and very long SFT stages can hurt the model's later RL trainability. Our midtraining stage sits between the two. It trains at 256K context for hundreds of billions of tokens on a diverse mixture of natural software-engineering data like packed repositories, pull requests, commit histories, and build logs together with a small share of agentic traces. On Nemotron 3.5 Lightning, 500B midtraining tokens improve SWE-bench Verified from 60.7 to 64.5, SWE-bench Multilingual from 55.8 to 62.8, and SWE-bench Pro from 32.0 to 35.5 after identical full SFT. On the larger Nemotron 3.5 Super, 280B midtraining tokens improve DeepSWE, a harder benchmark of long-horizon tasks, from 11.7 to 15.7 and SWE-bench Verified from 72.9 to 74.3 after identical full SFT. Midtraining also improves robustness across agent harnesses on both models. We report the ablations behind the recipe and an evaluation protocol built on short fixed SFT runs, since base-model proxy metrics did not correlate reliably with post-training results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
