Parametric Skills

Key points

The core problem is that in-context skills are limited by the model's ability to understand and follow instructions, and performance drops sharply, especially for open-source models or long contexts.
The solution is to feed a text skill into a hypernetwork that generates a LoRA adapter in a single forward pass, applying the skill without consuming context window.
It builds a 45.8K-scale high-quality skill library from web crawling and summaries of real agent trajectories, covering 13 domains.
On six SWE tasks, ParametricSkills scores 64.09 versus 57.65 for in-context learning and 48.48 for SHINE, averaged over LLM judges. SHINE fails to faithfully compress both the skill content and how to use it.
Rank-concatenation merging gives multi-skill composition performance equal to or better than a single LoRA, and it outperforms factor-wise linear merging.
In HumanEval-based self-evolving experiments, five rounds of iteration improve accuracy from 75% to 84.76%, and online continual merging further improves it from 29% to 51.6%.

External research summaries. These are not HDATF publications or measured product results.