Recursive Seed AI 25K: Instruction-Tuning Examples for Self-Improving LLMs

25,000 high-density instruction-tuning examples engineered to transform base large language models into Recursive Seed AIs. The dataset, created by author gss1147, is designed to enable capabilities like rigorous self-assessment and designing training recipes. It was last updated on Hugging Face in April 2026.

Use Cases

• Training models for rigorous self-assessment based on the described capability
• Developing LLMs that can design their own training recipes based on the dataset's purpose
• Creating autonomous evaluation frameworks based on the described target capability
• Maintaining safety and alignment constraints during capability pursuit based on the dataset's stated goal

Strengths

• Contains 25,000 examples, providing a substantial base for training
• Specifically engineered for a high-density instruction-tuning approach

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment

Provenance

Source

huggingface

Freshness

Last updated 2026-04-23 08:42:40; freshness should be verified