LLM Reasoning 相关度: 9/10

Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation

Zhuolin Yang, Zihan Liu, Yang Chen, Wenliang Dai, Boxin Wang, Sheng-Chieh Lin, Chankyu Lee, Yangyi Chen, Dongfu Jiang, Jiafan He, Renjie Pi, Grace Lam, Nayeon Lee, Alexander Bukharin, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping
arXiv: 2603.19220v1 发布: 2026-03-19 更新: 2026-03-19

AI 摘要

Nemotron-Cascade 2是一个30B MoE模型,通过级联强化学习和多领域知识蒸馏实现卓越的推理和Agent能力。

主要贡献

  • 构建了3B激活参数的30B MoE模型,具有卓越的推理和Agent能力
  • 扩展了Cascade RL,覆盖更广泛的推理和Agent领域
  • 引入了多领域On-Policy蒸馏,提升模型性能

方法论

通过SFT预训练后,使用Cascade RL进行强化学习,并通过多领域On-Policy蒸馏从教师模型中学习。

原文摘要

We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size, its mathematical and coding reasoning performance approaches that of frontier open models. It is the second open-weight LLM, after DeepSeekV3.2-Speciale-671B-A37B, to achieve Gold Medal-level performance in the 2025 International Mathematical Olympiad (IMO), the International Olympiad in Informatics (IOI), and the ICPC World Finals, demonstrating remarkably high intelligence density with 20x fewer parameters. In contrast to Nemotron-Cascade 1, the key technical advancements are as follows. After SFT on a meticulously curated dataset, we substantially expand Cascade RL to cover a much broader spectrum of reasoning and agentic domains. Furthermore, we introduce multi-domain on-policy distillation from the strongest intermediate teacher models for each domain throughout the Cascade RL process, allowing us to efficiently recover benchmark regressions and sustain strong performance gains along the way. We release the collection of model checkpoint and training data.

标签

MoE 强化学习 知识蒸馏 推理 Agent

arXiv 分类

cs.CL cs.AI cs.LG