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The emergence of Large Language Models (LLMs) such as ChatGPT and LLaMAencounter limitations in domain-specific tasks, with these models often lackingdepth and accuracy in specialized areas, and exhibiting a decrease in generalcapabilities when fine-tuned, particularly analysis ability in small sizedmodels. To address these gaps, we introduce ICE-GRT, utilizing ReinforcementLearning from Human Feedback (RLHF) grounded in Proximal Policy Optimization(PPO), demonstrating remarkable ability in in-domain scenarios withoutcompromising general task performance. Our exploration of ICE-GRT highlightsits understanding and reasoning ability to not only generate robust answers butalso to provide detailed analyses of the reasons behind the answer. Thiscapability marks a significant progression beyond the scope of SupervisedFine-Tuning models. The success of ICE-GRT is dependent on several crucialfactors, including Appropriate Data, Reward Size Scaling, KL-Control, AdvantageNormalization, etc. The ICE-GRT model exhibits state-of-the-art performance indomain-specific tasks and across 12 general Language tasks against equivalentsize and even larger size LLMs, highlighting the effectiveness of our approach.We provide a comprehensive analysis of the ICE-GRT, underscoring thesignificant advancements it brings to the field of LLM.

ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers