How do I install the Nemotron Cascade Rl skill?

Run npx skillmds@latest add adu2021/nemotron-cascade-rl in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Nemotron Cascade Rl safe to use?

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Which AI agents work with Nemotron Cascade Rl?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Nemotron Cascade Rl free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Nemotron Cascade Rl?

adu2021 (@adu2021) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Nemotron Cascade Rl by adu2021 — Agent Skill

Name: Nemotron Cascade Rl
Author: adu2021

Nemotron Cascade Rl

Train language models through sequential, domain-wise RL stages (RLHF → Instruction-Following → Math → Code → SWE) without catastrophic forgetting. Exploit policy-dependent training data distribution where previous behaviors persist when reward-relevant. 14B model surpasses DeepSeek-R1-0528 (671B) on LiveCodeBench.

adu2021 d0091e6 3.3 KB Updated Sep 17, 2026

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Skill Summary

Nemotron-Cascade introduces Cascade RL, departing from conventional mixed-domain RL by orchestrating sequential, domain-wise training stages. The approach applies reinforcement learning in progression—RLHF → Instruction-Following → Math → Code → SWE—each building upon previous stages without catastrophic forgetting. The key insight leverages policy-dependent training where previously learned behaviors persist when still reward-relevant, achieving state-of-the-art results where a 14B model surpasses DeepSeek-R1-0528 (671B) on LiveCodeBench.

When To Use

Training language models on multi-domain reasoning requiring progressive specialization
Projects where sequential RL stages prevent catastrophic forgetting across domains
Scenarios aiming for small unified models outperforming large domain-specific models
Research exploring cascaded RL as alternative to simultaneous multi-domain training

When NOT To Use

Single-domain reasoning tasks where cascaded complexity adds overhead without benefit
Scenarios requiring training all domains simultaneously for real-time adaptation
Projects with limited compute where progressive stages increase total training time
Domains with strong interference patterns between stages

Core Technique

The core innovation is Cascade RL, applying sequential domain-wise RL:

1. Sequential Domain-wise RL Apply reinforcement learning stages in cascading progression: RLHF → Instruction-Following → Math → Code → SWE. Each stage builds upon the previous without substantial performance degradation, leveraging domain ordering to enable progressive specialization.

2. Policy-Dependent Training Data Distribution Key structural insight distinguishes RL from supervised learning: "the training data distribution is policy-dependent; the LLM generates its own experience." This means previously learned behaviors persist when still reward-relevant, enabling positive transfer between stages.

3. Benefits from Earlier Stages Notably, RLHF substantially improves overall response quality in ways that benefit subsequent specialized reasoning tasks. Each stage enhances later stages through learned behavioral patterns.

4. Results Achieve state-of-the-art: 14B thinking model surpasses DeepSeek-R1-0528 (671B) on LiveCodeBench and achieves silver-medal performance at IOI 2025, demonstrating smaller unified models can match dedicated reasoning models through careful cascaded training.

Implementation Notes

Design sequential RL stages ordered by domain complexity/interference. Start with RLHF for general alignment. Progress through instruction-following, mathematical, coding, and engineering domains. Monitor for catastrophic forgetting and adjust rewards if performance regresses. Leverage positive transfer from earlier stages to enhance later specialization.

References

Original paper: Nemotron-Cascade (Dec 2025)
Cascade reinforcement learning frameworks
Multi-domain language model training

---name: nemotron-cascade-rldescription: Train language models through sequential, domain-wise RL stages (RLHF → Instruction-Following → Math → Code → SWE) without catastrophic forgetting. Exploit policy-dependent training data distribution where previous behaviors persist when reward-relevant. 14B model surpasses DeepSeek-R1-0528 (671B) on LiveCodeBench.license: MIT---## Skill SummaryNemotron-Cascade introduces Cascade RL, departing from conventional mixed-domain RL by orchestrating sequential, domain-wise training stages. The approach applies reinforcement learning in progression—RLHF → Instruction-Following → Math → Code → SWE—each building upon previous stages without catastrophic forgetting. The key insight leverages policy-dependent training where previously learned behaviors persist when still reward-relevant, achieving state-of-the-art results where a 14B model surpasses DeepSeek-R1-0528 (671B) on LiveCodeBench.## When To Use- Training language models on multi-domain reasoning requiring progressive specialization- Projects where sequential RL stages prevent catastrophic forgetting across domains- Scenarios aiming for small unified models outperforming large domain-specific models- Research exploring cascaded RL as alternative to simultaneous multi-domain training## When NOT To Use- Single-domain reasoning tasks where cascaded complexity adds overhead without benefit- Scenarios requiring training all domains simultaneously for real-time adaptation- Projects with limited compute where progressive stages increase total training time- Domains with strong interference patterns between stages## Core TechniqueThe core innovation is Cascade RL, applying sequential domain-wise RL:**1. Sequential Domain-wise RL**Apply reinforcement learning stages in cascading progression: RLHF → Instruction-Following → Math → Code → SWE. Each stage builds upon the previous without substantial performance degradation, leveraging domain ordering to enable progressive specialization.**2. Policy-Dependent Training Data Distribution**Key structural insight distinguishes RL from supervised learning: "the training data distribution is policy-dependent; the LLM generates its own experience." This means previously learned behaviors persist when still reward-relevant, enabling positive transfer between stages.**3. Benefits from Earlier Stages**Notably, RLHF substantially improves overall response quality in ways that benefit subsequent specialized reasoning tasks. Each stage enhances later stages through learned behavioral patterns.**4. Results**Achieve state-of-the-art: 14B thinking model surpasses DeepSeek-R1-0528 (671B) on LiveCodeBench and achieves silver-medal performance at IOI 2025, demonstrating smaller unified models can match dedicated reasoning models through careful cascaded training.## Implementation NotesDesign sequential RL stages ordered by domain complexity/interference. Start with RLHF for general alignment. Progress through instruction-following, mathematical, coding, and engineering domains. Monitor for catastrophic forgetting and adjust rewards if performance regresses. Leverage positive transfer from earlier stages to enhance later specialization.## References- Original paper: Nemotron-Cascade (Dec 2025)- Cascade reinforcement learning frameworks- Multi-domain language model training

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