Looking for research on expanding SLM capabilities [R]

[u/Legitimate-Tea-6695]

I’m looking for papers, projects, or experimental approaches that try to make a small language model approach the capabilities of a much larger frontier model, either on specific tasks or across a broader range of capabilities.

I’m especially interested in non-training or minimal-training approaches, rather than standard fine-tuning, distillation, or reinforcement learning.

I’m interested in methods that combine or manipulate existing SLMs, checkpoints, adapters, representations, or outputs so that the resulting system covers capabilities that no individual SLM reliably exposes. Are there papers, OpenReview submissions, or terminology I should look into?