Adopting artificial intelligence (AI) in radio access networks (RANs) presents several challenges, limited availability of link-level measurements (e.g., CQI reports), stringent real-time processing constraints (e.g., sub-1ms per TTI), and network heterogeneity (different spectrum bands, cell types, and vendor equipment). A critical yet often overlooked barrier lies in the computational and memory limitations of RAN baseband hardware—particularly in legacy 4th Generation (4G) systems—which typically lack on-chip neural accelerators. As a result, only lightweight AI models (under 1Mb and sub-100µs inference time) can be effectively deployed, limiting both their performance and applicability. However, achieving strong generalization across diverse network conditions often requires large-scale models with substantial resource demands. To address this trade-off, this paper investigates policy distillation in the context of a reinforcement learning–based link adaptation task. We explore two strategies: single-policy distillation, where a scenario-agnostic teacher model is compressed into one generalized student model; and multi-policy distillation, where multiple scenario-specific teachers are consolidated into a single generalist student. Experimental evaluations in a high-fidelity, 5th Generation (5G)-compliant simulator demonstrate that both strategies produce compact student models that preserve the teachers’ generalization capabilities while complying with the computational and memory limitations of existing RAN hardware.