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This research introduces TAPR, a reinforcement learning-based model designed to automatically optimize user prompts by reformulating them into task-specific instructions using Group Relative Policy Optimization (GRPO). By leveraging LLM-as-judge evaluations for reward signals, the method demonstrates consistent performance gains on standard benchmarks like Natural Questions and GSM8K when fine-tuning base models such as Phi-4-mini-instruct. The work directly addresses accessibility barriers in AI by enabling non-experts to achieve higher accuracy without manual prompt engineering expertise.
TAPR: Enhancing LLM Performance with a Task-Aware Prompt Rewriter
arXiv:2607.28657v1 Announce Type: new Abstract: Large Language Models (LLMs) often require carefully crafted prompts to unlock their full potential, which can be a barrier for non-expert users. This work addresses the challenge by introducing a Task-Aware Prompt Rewriter (TAPR), a model that reformulates user prompts into task-optimized prompts with the explicit goal of improving downstream LLM performance. We train TAPR using reinforcement learning with Group Relative Policy Optimization (GRPO), where rewards are derived from LLM-as-judge evaluations of both the reformulated prompt and the corresponding task output. Experimental results on diverse tasks, such as question answering, summarization, and arithmetic reasoning, show that our method yields consistent gains over base models in prompt rewriting ability. Fine-tuning Phi-4-mini-instruct (as the base model for TAPR) produces prompts that contain clearer and more instructive language, leading to higher accuracy on established benchmarks such as Natural Questions and GSM8K. Our code is available at: https://github.com/OliverSavolainen/task-specific-prompt-rewriter
Abstract:Large Language Models (LLMs) often require carefully crafted prompts to unlock their full potential, which can be a barrier for non-expert users. This work addresses the challenge by introducing a Task-Aware Prompt Rewriter (TAPR), a model that reformulates user prompts into task-optimized prompts with the explicit goal of improving downstream LLM performance. We train TAPR using reinforcement learning with Group Relative Policy Optimization (GRPO), where rewards are derived from LLM-as-judge evaluations of both the reformulated prompt and the corresponding task output. Experimental results on diverse tasks, such as question answering, summarization, and arithmetic reasoning, show that our method yields consistent gains over base models in prompt rewriting ability. Fine-tuning Phi-4-mini-instruct (as the base model for TAPR) produces prompts that contain clearer and more instructive language, leading to higher accuracy on established benchmarks such as Natural Questions and GSM8K. Our code is available at: this https URL
Artificial Intelligence (cs.AI)
arXiv:2607.28657 [cs.AI]
(or arXiv:2607.28657v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.28657 Focus to learn more arXiv-issued DOI via DataCite
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