Use Of User Feedback for Adaptive Model Tuning

This paper discusses a possible path toward adaptive finetuning of large-scale language models over user signal continual learning. In our study, we are trying to organize an approach for explicit and implicit channels. Inside channel heterogeneous feedback filtering, interpreting, and integrating all of them into one regular tuning cycle that would help keep the model updated and qualitative in real-time usage. This paper validates these claims with studies of how fast static model parameterizations get outdated on one hand, and an observation limitation from classic offline process drops in answer accuracy and user trust on the other hand. This unification is novel because it unifies three classes of feedback into a multi-objective loss function with dynamic weights thereof; implemented through a microservice hierarchy architecture that logs, streams filtering, anonymizing, annotating data — then trains in several stages including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) plus contextual bandit plus rolling A/B test with confidence bounds. In fact, after going through several iterations of SFT and RLHF, it is the live model that steadily beats by a good margin all static baselines in terms of human preference. At the same time, the contextual bandit reduces average regret in online mode, and scaling to billions of queries is achieved without loss of metadata integrity or update flexibility. Key challenges are identified: catastrophic forgetting of rare skills, narrow-group preference bias, privacy risks when processing live data, and high manual annotation costs, for which regularization, stratified sampling, differential privacy, and active self-evaluation learning are proposed as solutions. This article should interest and benefit those who investigate and architect systems for natural language, machine learning, and recommendation engines.