Self-Training Elicits Concise Reasoning in Large Language Models
Bibliographic record
Abstract
Chain-of-thought (CoT) reasoning has dramatically improved large language model performance on complex tasks. While recent "thinking'' models, like OpenAI's o1, use substantial inference budgets to further boost performance, this raises concerns about inference efficiency due to potential redundancy. We hypothesize that LLMs have a latent capacity for more concise reasoning, evidenced by shorter, correct reasoning paths in their outputs. To elicit this capacity, we introduce fine-tuning methods leveraging self-generated data from Best-of-N sampling and few-shot conditioning. Our few-shot conditioned best-of-N sampling (FS-BoN) method significantly reduces reasoning length by 30%, or 2.4x more than previous baselines while maintaining accuracy. This implies fine-tuning with curated self-generated data can unlock latent concise reasoning, enabling more efficient inference.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".