Can you recommend an annotation tool for safety labeling in RLHF data preparation?

Yes — for RLHF safety labeling, I’d recommend Label Studio as the best general-purpose option.

Why Label Studio

• Flexible schemas: good for ranking, binary safety labels, toxicity categories, policy violations, etc.
• Custom UI: you can tailor interfaces for pairwise comparison, multi-label classification, or rubric-based review.
• Open source + self-hostable: helpful if your data is sensitive.
• API/integration friendly: works well with active learning or programmatic pre-labeling.

Good alternatives depending on your needs

• Prodigy: great if you want a more developer-centric, fast annotation workflow, especially for NLP.
• Argilla: strong for LLM/RLHF-style feedback loops, dataset curation, and human-in-the-loop workflows.
• Doccano: simpler and lightweight, good for standard text classification and sequence labeling.
• Scale AI / Surge AI / Sama: better if you want managed annotation services rather than building your own pipeline.

My practical recommendation

• If you want open-source and customizable: Label Studio
• If you want LLM-centric review workflows: Argilla
• If you want fast internal annotation for NLP engineers: Prodigy
• If you want outsourced labeling at scale: Scale AI or similar managed vendors

If you want, I can also recommend the best tool based on your specific setup:

• text-only vs multimodal
• pairwise preference vs safety taxonomy labels
• self-hosted vs vendor-managed
• team size and budget