NVIDIA Releases Nemotron-Cascade 2: An Open 30B MoE with 3B Active Parameters, Delivering Better Reasoning and Strong Agentic Capabilities

NVIDIA has announced the release of

Nemotron-Cascade 2

, an open-weight

30B Mixture-of-Experts (MoE)

model with

3B activated parameters

. The model focuses on maximizing ‘intelligence density,’ delivering advanced reasoning capabilities at a fraction of the parameter scale used by frontier models. Nemotron-Cascade 2 is the second open-weight LLM to achieve

Gold Medal-level performance

in the 2025 International Mathematical Olympiad (IMO), the International Olympiad in Informatics (IOI), and the ICPC World Finals.

Targeted Performance and Strategic Trade-offs

The primary value proposition of Nemotron-Cascade 2 is its specialized performance in mathematical reasoning, coding, alignment, and instruction following. While it achieves state-of-the-art results in these key reasoning-intensive domains, it is surely not a ‘blanket win’ across all benchmarks.

The model’s performance excels in several targeted categories compared to the recently released

Qwen3.5-35B-A3B

(February 2026) and the larger

Nemotron-3-Super-120B-A12B



:

• 
  
  Mathematical Reasoning:
  
  Outperforms Qwen3.5-35B-A3B on
  
  AIME 2025
  
  (92.4 vs. 91.9) and
  
  HMMT Feb25
  
  (94.6 vs. 89.0).
  


• 
  
  Coding:
  
  Leads on
  
  LiveCodeBench v6
  
  (87.2 vs. 74.6) and
  
  IOI 2025
  
  (439.28 vs. 348.6+).
  


• 
  
  Alignment and Instruction Following:
  
  Scores significantly higher on
  
  ArenaHard v2
  
  (83.5 vs. 65.4+) and
  
  IFBench
  
  (82.9 vs. 70.2).
  

Technical Architecture: Cascade RL and Multi-domain On-Policy Distillation

(

MOPD

)

The model’s reasoning capabilities stem from its post-training pipeline, starting from the

Nemotron-3-Nano-30B-A3B-Base

model


.

1. Supervised Fine-Tuning (SFT)

During SFT, NVIDIA research team utilized a meticulously curated dataset where samples were packed into sequences of up to

256K tokens

.

The dataset included:

• 
  
  1.9M Python reasoning traces
  
  and 1.3M Python tool-calling samples for competitive coding.
  


• 
  
  816K samples
  
  for mathematical natural language proofs.
  


• 
  A specialized
  
  Software Engineering (SWE) blend
  
  consisting of 125K agentic and 389K agentless samples.
  

2. Cascade Reinforcement Learning

Following SFT, the model underwent

Cascade RL

, which applies sequential, domain-wise training


. This prevents catastrophic forgetting by allowing hyperparameters to be tailored to specific domains without destabilizing others








. The pipeline includes stages for instruction-following (IF-RL), multi-domain RL, RLHF, long-context RL, and specialized Code and SWE RL








.

3. Multi-Domain On-Policy Distillation (MOPD)

A critical innovation in Nemotron-Cascade 2 is the integration of

MOPD

during the Cascade RL process. MOPD assembly uses the best-performing intermediate ‘teacher’ models—already derived from the same SFT initialization—to provide a dense token-level distillation advantage.

This advantage is defined mathematically as:

The research team found that MOPD is substantially more sample-efficient than sequence-level reward algorithms like

Group Relative Policy Optimization (GRPO)

. For instance, on

AIME25

, MOPD reached teacher-level performance (92.0) within 30 steps, while GRPO achieved only 91.0 after matching those steps.

Inference Features and Agentic Interaction

Nemotron-Cascade 2 supports two primary operating modes through its chat template:

• 
  
  Thinking Mode:
  
  Initiated by a single
  
<think>

  token, followed by a newline. This activates deep reasoning for complex math and code tasks.
  


• 
  
  Non-Thinking Mode:
  
  Activated by prepending an empty
  
<think></think>

  block for more efficient, direct responses.
  

For agentic tasks, the model utilizes a structured tool-calling protocol within the system prompt








. Available tools are listed within

<tools>

tags, and the model is instructed to perform tool calls wrapped in

<tool_call>

tags to ensure verifiable execution feedback








.

By focusing on ‘intelligence density,’ Nemotron-Cascade 2 demonstrates that specialized reasoning capabilities once thought to be the exclusive domain of frontier-scale models are achievable at a 30B scale through domain-specific reinforcement learning.

Check out


Paper


and


Model on HF

.

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