Fora preserves LLM capabilities by protecting function‑space, not just weights
Full fine‑tuning often erodes skills a model already has. Fora estimates each layer’s activation subspace and blocks updates from touching those directions, keeping learned functions intact while still allowing new task learning. Experiments on Qwen‑3‑1.7B show markedly better capability retention than weight‑space tricks with minimal performance loss.