Hugging Face Overview
Overview
nvidia/Nemotron-Orchestrator-8B is an 8 billion parameter orchestration model developed by NVIDIA and the University of Hong Kong. It is specifically designed to solve complex, multi-turn agentic tasks by intelligently coordinating a diverse set of expert models and tools. The model is built on the Qwen3-8B base and is intended for research and development purposes.
Key Capabilities
- Intelligent Orchestration: Manages heterogeneous toolsets, including basic tools (search, code execution) and other specialized or generalist LLMs.
- Multi-Objective RL Training: Utilizes Group Relative Policy Optimization (GRPO) with a novel reward function to optimize for accuracy, latency/cost, and adherence to user preferences.
- Efficiency: Delivers higher accuracy at significantly lower computational cost compared to monolithic frontier models, achieving 2.5x faster performance and 30% monetary cost compared to GPT-5 on HLE.
- Robust Generalization: Demonstrates the ability to generalize to unseen tools and pricing configurations.
Performance Highlights
On the Humanity's Last Exam (HLE) benchmark, Orchestrator-8B achieves a score of 37.1%, surpassing GPT-5 (35.1%). It also consistently outperforms strong monolithic systems like GPT-5, Claude Opus 4.1, and Qwen3-235B-A22B on HLE, FRAMES, and \u03c4\u00b2-Bench, showcasing versatile reasoning and robust tool orchestration with substantially lower cost.
Good For
- Developers and researchers working on complex agentic systems requiring efficient coordination of multiple models and tools.
- Applications where optimizing for accuracy, cost, and latency simultaneously is critical.
- Scenarios demanding robust generalization to new tools and dynamic pricing environments.