Meta-Merging by Checkpoint Nowcasting

Model merging---the direct combination of parameters from independently fine-tuned networks---offers a way to compose task-specific capabilities without retraining or ensemble inference. Existing merge methods are often built from hand-crafted arithmetic or sparsification heuristics, leaving open whether general learned weight-space operators can be repurposed for merging directly. We study this question with NiNo, a pre-trained checkpoint-nowcasting meta-network originally designed to predict near-future training states from short checkpoint histories. We show that pre-trained NiNo can be reused as a data-free pairwise meta-merge operator for independently fine-tuned models. On an 8-task CLIP ViT-B/16 benchmark, NiNo is competitive with strong arithmetic baselines and consistently lands in the same functional region as weight averaging, Task Arithmetic, and TIES. Moreover, NiNo is best on HumanEval in a Qwen3 language extension among the compared merge methods, while extending meta-merge beyond pairs remains an open challenge. These results position learned checkpoint nowcasting as a practical starting point for data-free model merging and motivate future weight-space learners trained for merging explicitly.