New Google DeepMind Paper.

Model weights do not have to remain static artifacts that can only be fine-tuned or averaged.

A new capability can now be initialized from both what an agent wrote down and what it previously learned into weights.

SkillSmith treats prefix key-value caches as another input modality.

For a new task， it interleaves existing prefix weights with task descriptions， examples， and a rationale explaining how the source capabilities relate to the target.

A forward pass then produces a fresh prefix cache for a frozen Gemma 3 4B model， so the initial composition happens at inference time rather than through a new target-specific training run.

The cleanest evidence is the ablation： on Composite-SNI， Elo is 1，455 with only K-V caches， 1，622 with only text， and 1，714 with both.

The gain cannot be explained by richer prompting or a learned weight merger alone.

The generated prefixes also give downstream fine-tuning a stronger starting point， especially on difficult， data-sparse MMLU-ProX tasks.

The boundary matters： on simpler SNI tasks with roughly 1，000 examples each， fine-tuned methods converge.

SkillSmith does not remove training， but it changes where adaptation starts： previous text and previous weights can now be composed into the initialization for a new capability.

• arxiv. org/abs/2607.27497

Title： "SkillSmith： Learning to Compose Parametric Skills and Textual Knowledge"