Title: Contrastive-Online-Meta (COM): A Dynamic Adaptation Mechanism for Instruction-Tuned CodeLLMs
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Instruction-Tuned CodeLLMs

Abstract: We propose Contrastive-Online-Meta (COM), a dynamic adaptation framework for instruction-tuned CodeLLMs that coefficients to the issues of catastrophic forgetting and noisy feedback at the time of deployment. The framework combines contrastive pre-training and online meta-learning to separate the task-invariant representation learning and fast adaptation, which helps preserve core programming knowledge while achieving real-time adaptation. A contrastive pre-training module takes a first step at clustering semantically similar instructions and unionizing dissimilar ones, to guarantee its robustness to task variations. During inference, an online meta-learner takes pairs of instruction-feedback streaming and does a light-weight gradient-based update to meta-parameters, which dynamically adjust the model behavior in a way that does not destabilize the pre-trained behavior-effective thing. Furthermore, the dynamic memory buffer simply retains coherence with recent interactions by deriving pairs stored in the buffer for the sake of contrastive match. Unlike monolithic fine-tuning or prompt engineering, COM explicitly separates the processes of representation learning and adaptation, hence avoiding forgetting and overfitting. Experiments using benchmark datasets show that the framework has a better capacity for adaptation efficiency and task generalization than static and incremental tuning baselines. The proposed method fills in the missing link between the offline pre-training and the online accelerated deployment, which provides a scalable solution to real-world code generation systems that require continuous learning. And, its modular nature also supports integration with existing CodeLLMs, which makes it practical for different programming assistance scenarios.