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In this study, we investigate the enhancement of the GPT Neo 125M performancein Community Question Answering (CQA) with a focus on programming, through theintegration of Reinforcement Learning from Human Feedback (RLHF) and theutilization of scores from Stack Overflow. Two distinct reward model trainingstrategies are employed for fine-tuning with Proximal Policy Optimization(PPO). Notably, the improvements in performance achieved through this methodare comparable to those of GPT Neo 2.7B parameter variant. Additionally, anauxiliary scoring mechanism is introduced, which demonstrates the limitationsof conventional linguistic metrics in evaluating responses in the programmingdomain. Through accurate analysis, this paper looks at the divergence betweentraditional linguistic metrics and our human-preferences-based reward model,underscoring the imperative for domain-specific evaluation methods. Byelucidating the complexities involved in applying RLHF to programming CQA andaccentuating the significance of context-aware evaluation, this studycontributes to the ongoing efforts in refining Large Language Models throughfocused human feedback.

Reinforcement learning for question answering in programming domain using public community scoring as a human feedback