Research map / Large language models / Reasoning and chain of thought
STaR: Bootstrapping Reasoning With Reasoning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add rationalization (answer-hinted rationale generation for failed problems) (ablation) | STaR · CommonsenseQA dev | accuracy: 68.8 -> 72.5 | Helped |
| add rationalization (ablation) | STaR · GSM8K test | accuracy: 10.1 -> 10.7 | No clear effect |
| STaR rationale bootstrapping vs direct-answer finetuning | GPT-J · CommonsenseQA dev | accuracy: 60.0 -> 72.5 | Helped |
| STaR vs direct-answer finetuning | GPT-J · GSM8K test | accuracy: 5.8 -> 10.7 | Helped |
| STaR vs direct finetuning without rationales | GPT-J · Arithmetic | accuracy: 76.3% -> 89.5% | Helped |
| include few-shot prompts during fine-tuning (ablation) | STaR without rationalization · CommonsenseQA dev | accuracy: 60.9% -> 68.8% | Helped |
| include few-shot prompts during fine-tuning (ablation) | STaR with rationalization · CommonsenseQA dev | accuracy: 69.9% -> 72.5% | Helped |
| use higher sampling temperature (0.5 or 0.7) instead of rationalization (ablation) | STaR · Arithmetic / CQA | accuracy | Hurt |
Closest papers by meaning
- Tailoring Self-Rationalizers with Multi-Reward Distillation (ICLR 2024 · similarity 0.89)
- BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning (ICML 2025 · similarity 0.88)
- General-Reasoner: Advancing LLM Reasoning Across All Domains (NeurIPS 2025 · similarity 0.88)
- Decompose, Analyze and Rethink: Solving Intricate Problems with Human-like Reasoning Cycle (NeurIPS 2024 · similarity 0.88)
- SuperCorrect: Advancing Small LLM Reasoning with Thought Template Distillation and Self-Correction (ICLR 2025 · similarity 0.88)
- Improving Factuality and Reasoning in Language Models through Multiagent Debate (ICML 2024 · similarity 0.88)
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