STaR: Bootstrapping Reasoning With Reasoning
by Stanford University / Google Brain · free · Last verified 2026-03-17
STaR (Self-Taught Reasoner) is a research paper introducing an iterative bootstrapping method for language models. The model learns to improve its reasoning abilities by generating rationales for problems, filtering out the incorrect ones, and then fine-tuning itself on the successfully reasoned examples. This allows smaller models to achieve reasoning performance comparable to much larger ones.
https://arxiv.org/abs/2203.14465 ↗B
B—Above Average
Adoption: B+Quality: A+Freshness: B+Citations: B+Engagement: F
Specifications
- License
- Open Access
- Pricing
- free
- Capabilities
- Iterative Self-Improvement, Rationale Generation (Chain-of-Thought), Bootstrapping from a small set of examples, Solving mathematical word problems (e.g., GSM8K), Few-shot learning enhancement, Fine-tuning on self-generated data, Commonsense reasoning (e.g., CommonsenseQA)
- Integrations
- Use Cases
- [object Object], [object Object], [object Object], [object Object]
- API Available
- No
- Tags
- star, self-taught-reasoner, bootstrapping, reasoning, rationale-generation, iterative-learning, self-improvement, chain-of-thought, language-models, ai-research, fine-tuning
- Added
- 2026-03-17
- Completeness
- 1%
Index Score
67.5Adoption
75
Quality
90
Freshness
72
Citations
78
Engagement
0