Minzhi Li, William Held, Michael J. Ryan, Kunat Pipatanakul, Potsawee Manakul, Hao Zhu, Diyi Yang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

This paper investigates prior prompt engineering (pPE) in the context of reinforcement finetuning (RFT), where language models (LMs) are incentivized to exhibit behaviors that maximize performance through reward signals.While existing RFT research has primarily focused on algorithms, reward shaping, and data curation, the design of the prior prompt-the instructions prepended to queries during training to elicit behaviors such as step-by-step reasoning-remains underexplored.We investigate whether different pPE approaches can guide LMs to internalize distinct behaviors after RFT.Inspired by inference-time prompt engineering (iPE), we translate five representative iPE strategies-reasoning, planning, codebased reasoning, knowledge recall, and nullexample utilization-into corresponding pPE approaches.We experiment with Qwen2.5-7B using each of the pPE approaches, then evaluate performance on in-domain and out-of-domain benchmz arks (e.g., AIME2024, HumanEval+, and GPQA-Diamond).Our results show that all pPE-trained models surpass their iPE-prompted counterparts, with the null-example pPE approach achieving the largest average performance gain and the highest improvement on AIME2024 and GPQA-Diamond, surpassing the commonly used reasoning approach.Furthermore, by adapting a behavior-classification framework, we demonstrate that different pPE strategies instill distinct behavioral styles in the resulting models.These findings position pPE as a powerful yet understudied axis for RFT.