Recursive Seed AI 25K: 25,000 Examples for Self-Improving LLM Training

25,000 high-density instruction-tuning examples engineered by WithinUsAI to transform base large language models. The dataset, last updated in April 2026, is designed to create models capable of rigorous self-assessment and autonomous improvement. It aims to foster capabilities in designing training recipes, proposing architectural changes, and maintaining safety constraints.

Use Cases

• Instruction-tuning base LLMs for self-assessment based on the described training objective.
• Developing autonomous AI evaluation frameworks based on the dataset's engineered examples.
• Researching AI safety and alignment constraints within self-improving systems based on the described focus.
• Exploring methods for LLMs to design their own training data and architectural improvements.

Strengths

• Contains 25,000 high-density examples specifically for instruction-tuning.
• Explicitly engineered for the advanced objective of creating Recursive Seed AI models.
• Last updated on 2026-04-23, suggesting recent maintenance.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is known but other scale details like file formats and size are unknown.
• The description is promotional; actual data quality and content require manual inspection.

Provenance

Source

WithinUsAI via Hugging Face.

Collection Method

Engineered for instruction-tuning; specific gathering method is not detailed.

Time Range

Creation or update timeframe not specified beyond the last updated date.

Freshness

Last updated 2026-04-23 08:42:40.

Geography

Spatial coverage is not specified.