[Paper] Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation
Source: arXiv - 2603.19220v1
Overview
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.
Key Contributions
This paper presents research in the following areas:
- cs.CL
- cs.AI
- cs.LG
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.CL.
Authors
- 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
Paper Information
- arXiv ID: 2603.19220v1
- Categories: cs.CL, cs.AI, cs.LG
- Published: March 19, 2026
- PDF: Download PDF