Post-Training Evaluation Pipelines for Measuring LLM Performance in Coding and Logical Reasoning

Abstract

The advancement of large language models (LLMs) has demonstrated significant potential in domains requiring coding proficiency and logical reasoning. Post-training evaluation pipelines are critical in measuring the performance of these models after Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). These evaluation pipelines must be methodologically robust to ensure accurate assessment across diverse coding tasks and logical reasoning scenarios. This paper introduces a comprehensive framework for designing and implementing post-training evaluation pipelines tailored to LLMs' performance in coding and reasoning domains. The framework leverages AI-assisted tools, including Codex-Eval, to quantify code quality, logical consistency, and alignment with user expectations.

The proposed framework is divided into three primary components: task design, metric definition, and iterative optimization. Task design focuses on creating diverse coding and reasoning challenges, ensuring adequate coverage of complexity levels and real-world scenarios. Metric definition involves defining both quantitative and qualitative metrics, such as functional correctness, code efficiency, adherence to style guidelines, logical coherence, and deductive reasoning accuracy. This stage includes adopting automated evaluation metrics (e.g., BLEU, ROUGE, and perplexity) and human-in-the-loop assessments to capture nuanced performance dimensions. Iterative optimization utilizes feedback from the evaluation process to enhance LLMs iteratively, employing mechanisms like reinforcement learning to address observed deficiencies.

A significant innovation of this framework is the integration of Codex-Eval and similar tools. These tools automate the grading of generated code based on syntax correctness, functional output, and adherence to predefined coding standards. Furthermore, advanced logical reasoning benchmarks assess the LLMs' capacity to generalize across unseen reasoning tasks, highlighting their adaptability and robustness. This dual evaluation ensures a holistic understanding of the LLM's strengths and limitations.

Case studies of widely used LLMs, such as GPT-4 and Codex, are presented to validate the framework's efficacy. The results reveal the critical role of task diversity and robust metrics in capturing performance subtleties. The study also underscores the challenges of generalizing across diverse logical reasoning paradigms and the impact of RLHF in refining model alignment to user expectations. Additionally, practical strategies for iterative optimization are detailed, emphasizing the significance of fine-tuning cycles informed by granular evaluation outcomes.

Despite its strengths, the framework also acknowledges several challenges. These include the computational overhead associated with large-scale evaluation, the need for extensive annotated datasets, and the potential biases in both automated tools and human evaluators. The paper concludes by suggesting future directions, including the development of lightweight evaluation tools, the standardization of evaluation metrics, and the exploration of cross-disciplinary benchmarks that integrate coding and reasoning assessments.

By offering a rigorous and adaptable post-training evaluation framework, this study contributes to the growing body of literature on assessing LLM performance in specialized domains. It highlights the need for continuous refinement of evaluation methodologies to keep pace with the rapid evolution of LLM architectures and training paradigms. Ultimately, this research underscores the importance of meticulous post-training evaluations in advancing the practical utility of LLMs in coding and logical reasoning tasks.