WST: Weak-to-Strong Knowledge Transfer via Reinforcement Learning

Haosen Ge; Shuo Li; Lianghuan Huang

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arXiv · 2508.16741

WST: Weak-to-Strong Knowledge Transfer via Reinforcement Learning

Haosen Ge·Shuo Li·Lianghuan Huang

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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.

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BibTeX RIS

Haosen Ge, Shuo Li, Lianghuan Huang. 2025-08-22. WST: Weak-to-Strong Knowledge Transfer via Reinforcement Learning. https://arxiv.org/abs/2508.16741

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