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NVIDIA Advanced
Nemotron-4 340B Reward
Nemotron-4 340B Reward is NVIDIA's 4K-context English reward model for RLAIF, response ranking and synthetic-data filtering.
Language Models (LLMs)Text Freemium
What is this model and why does it matter?
Nemotron-4 340B Reward is NVIDIA's large reward model for scoring responses across HelpSteer-style preference dimensions.
Reward modelingRLAIFSynthetic-data filteringResponse rankingModel-as-a-judge research
Nemotron-4 340B Reward: features, use cases and important details
Nemotron-4 340B Reward is NVIDIA’s reward-model variant of the Nemotron-4 340B family.
Verified model facts
NVIDIA documents a 4,096-token context window, June 2023 data cutoff and English-language preference scoring trained with HelpSteer2.
Current status
The checkpoint remains available under the NVIDIA Open Model License and is intended for RLAIF, alignment and model-as-a-judge workflows.
Best fit
It fits reward modeling, synthetic-data filtering and ranking candidate responses before downstream training.
Limitations
It is not a text-generation assistant, requires very large hardware and its learned reward signal should be validated against human preferences.
How to use this model
- Review NVIDIA's model license.
- Download or deploy the official reward checkpoint.
- Send prompt-response text in the documented reward-model format.
- Use returned scalar scores for ranking or filtering.
- Validate the reward signal against human evaluation before optimization.
Example prompts
Score these candidate responses for helpfulness and correctness.Rank synthetic responses before adding them to a training dataset.
What it can do
- Reward scoring
- RLAIF
- Preference modeling
- Synthetic-data filtering
- Response ranking
Practical use cases
- Alignment pipelines
- Dataset filtering
- Reward benchmarking
- Judge-model experiments
What does it cost?
Reward-model hosted access is linked from NVIDIA's current model card; no current usage price is asserted.
Simple summaryThe model requires very large GPU infrastructure for local use; NVIDIA lists multi-node H100/A100-class configurations.
What stands out
- Official NVIDIA reward model
- Commercially usable
- Multi-dimensional preference scoring
Things to consider
- 340B scale
- English-only optimization
- Not a generative assistant
Important restrictions and trade-offs
- Reward scores can encode training-data biases
- Very expensive to self-host
- Should not replace human evaluation in high-stakes selection
Our editorial take
A specialized alignment component, not a chat model; useful when the page clearly explains that its output is reward scores rather than generated answers.
Primary sources
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