Exploring how strategic prompt engineering during training can internalize complex reasoning behaviors in language models, moving beyond traditional inference-time approaches.
Prior Prompt Engineering (pPE) represents a paradigm shift in how we approach Reinforcement Fine-Tuning (RFT) for Language Models. Unlike traditional inference-time prompt engineering, which aims to elicit specific behaviors during deployment, pPE focuses on internalizing desired behaviors directly into the model's fundamental response patterns.
The research translates successful inference-time strategies—such as Chain-of-Thought reasoning, Plan-and-Solve, and Program-of-Thought—into training-time prompts that shape the model's intrinsic problem-solving methodology.
The motivation stems from recognizing that while existing RFT research has concentrated on optimizing algorithms, reward functions, and data curation, the systematic design of instructional prompts used during training has remained relatively underexplored. This gap represents a significant opportunity to enhance LM capabilities.
The study systematically evaluates five distinct pPE strategies, each translated from successful inference-time prompt engineering techniques. These strategies are designed to instill specific problem-solving behaviors in language models during the RFT process.
Inspired by Chain-of-Thought (CoT)
Encourages explicit, step-by-step reasoning before arriving at final answers, cultivating systematic problem decomposition.
Based on Plan-and-Solve (PS)
Guides models to formulate high-level strategies and roadmaps before executing solution approaches.
Derived from Program-of-Thought (PoT)
Leverages programming code as a tool for algorithmic problem-solving and computational thinking.
From Generated Knowledge Prompting
Prompts explicit retrieval and articulation of relevant facts, definitions, or formulas before problem-solving.
Based on Null-shot Prompting
Encourages generating illustrative examples or counterexamples to understand problem boundaries and patterns.
7-billion parameter language model
All pPE variants were trained using the same base model to ensure fair comparison and isolate the effects of different prompt engineering strategies.
Accuracy + Format adherence
Language Models trained using pPE strategies consistently outperformed their inference-time prompted counterparts across all evaluated benchmarks, demonstrating the superiority of internalizing behaviors during training.
Achieved largest average performance gain
Each pPE strategy instills unique problem-solving approaches
The research demonstrates that different pPE strategies lead to observable differences in how models approach tasks, effectively creating specialized AI "thinking" modes.
Performance improvements extended beyond the training domain (mathematics) to other areas:
Enhanced performance on graduate-level reasoning tasks requiring deep understanding
Improved code generation and functional correctness
pPE represents a powerful, previously underexplored axis within Reinforcement Fine-Tuning, opening new avenues for model optimization beyond traditional algorithmic and reward-focused approaches.
The systematic engineering of prior prompts during training can fundamentally shape model capabilities and problem-solving approaches.
The ability to cultivate diverse cognitive styles through pPE enables the creation of specialized LMs, each optimized for different problem-solving approaches and task types.
RFT demonstrates robustness in discovering useful generation patterns that generalize across domains, suggesting it functions as a mechanism for learning transferable problem-solving strategies.
Mathematical training improved performance on coding and general reasoning tasks
First comprehensive exploration of Prior Prompt Engineering's impact on model performance and behavior during RFT
Successfully translated five inference-time prompting strategies into effective training-time pPE approaches
Identified Null-example Utilization as the most effective pPE strategy, achieving largest performance gains on challenging benchmarks
Demonstrated that different pPE strategies cultivate distinct AI "thinking" modes and problem-solving styles
This research positions Prior Prompt Engineering as a fundamental tool for shaping AI capabilities, offering a pathway to more specialized, robust, and adaptable language models. The success of pPE suggests that how we prompt models during training is as crucial as the algorithms and rewards we use.
As we advance toward more sophisticated AI systems, pPE provides a mechanism to intentionally design not just what models can do, but how they think about solving problems.