{"ID":2879559,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16741","arxiv_id":"2508.16741","title":"WST: Weak-to-Strong Knowledge Transfer via Reinforcement Learning","abstract":"Effective prompt engineering remains a challenging task for many applications. We introduce Weak-to-Strong Transfer (WST), an automatic prompt engineering framework where a small \"Teacher\" model generates instructions that enhance the performance of a much larger \"Student\" model. Unlike prior work, WST requires only a weak teacher, making it efficient and broadly applicable in settings where large models are closed-source or difficult to fine-tune. Using reinforcement learning, the Teacher Model's instructions are iteratively improved based on the Student Model's outcomes, yielding substantial gains across reasoning (MATH-500, GSM8K) and alignment (HH-RLHF) benchmarks - 98% on MATH-500 and 134% on HH-RLHF - and surpassing baselines such as GPT-4o-mini and Llama-70B. These results demonstrate that small models can reliably scaffold larger ones, unlocking latent capabilities while avoiding misleading prompts that stronger teachers may introduce, establishing WST as a scalable solution for efficient and safe LLM prompt refinement.","short_abstract":"Effective prompt engineering remains a challenging task for many applications. We introduce Weak-to-Strong Transfer (WST), an automatic prompt engineering framework where a small \"Teacher\" model generates instructions that enhance the performance of a much larger \"Student\" model. Unlike prior work, WST requires only a...","url_abs":"https://arxiv.org/abs/2508.16741","url_pdf":"https://arxiv.org/pdf/2508.16741v1","authors":"[\"Haosen Ge\",\"Shuo Li\",\"Lianghuan Huang\"]","published":"2025-08-22T18:33:06Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\",\"Large Language Model\",\"RLHF\"]","has_code":false}