[2604.01601] Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling

Computer Science > Machine Learning arXiv:2604.01601 (cs) [Submitted on 2 Apr 2026] Title:Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling Authors:Deeptanshu Malu, Deevyanshu Malu, Aditya Nemiwal, Sunita Sarawagi View a PDF of the paper titled Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling, by Deeptanshu Malu and 3 other authors View PDF HTML (experimental) Abstract:We investigate training strategies that co-develop in-context learning (ICL) and in-weights learning (IWL), and the ability to switch between them based on context relevance. Although current LLMs exhibit both modes, standard task-specific fine-tuning often erodes ICL, motivating IC-Train - fine-tuning with in-context examples. Prior work has shown that emergence of ICL after IC-Train depends on factors such as task diversity and training duration. In this paper we show that the similarity structure between target inputs and context examples also plays an important role. Random context leads to loss of ICL and IWL dominance, while only similar examples in context causes ICL to degenerate to copying labels without regard to relevance. To address this, we propose a simple Contrastive-Context which enforces two types of contrasts: (1) mix of similar and random examples within a context to evolve a correct form of ICL, and (2) varying grades of similarity across contexts to evolve ICL-IWL mixtures. We present insights on the importance of such contrast with ...