Parametric Skills

2026-06-29

AI Summary

This work addresses the challenge that large language models struggle to effectively follow textual skill instructions in long-context scenarios, which hinders practical skill deployment. To overcome this limitation, the authors propose ParametricSkills, a novel framework that, for the first time, dynamically converts free-form textual skills into LoRA adapter parameters at test time, enabling context-independent skill invocation and establishing a new paradigm for test-time continual learning. Leveraging a large-scale skill repository and invocation trajectories derived from OpenCode, a hypernetwork is trained to map textual descriptions to parameterized skills. Evaluated across six software engineering subtasks, the method outperforms in-context learning by an average of 6.44 points under DeepSeek-V4-Flash assessment, with significant improvements in both BERTScore and F1 metrics.

Abstract

Since intelligence fundamentally relies on efficient skill acquisition (Chollet, 2019), the ability to leverage skills is critical. For LLMs, skills, manually authored or extracted from task trajectories, are textual recipes encoding mature problem-solving experience and are critical to agentic capabilities. Despite widespread deployment, their utility is limited by the model's ability to comprehend and follow skill instructions, especially under complex and long-context scenarios, where key instructions are difficult to locate and adhere to. To address this limitation, we propose ParametricSkills, a framework that can convert free-form textual skills into parameters at test time, enabling context-free skill exploitation. Specifically, we first construct a large-scale, high-quality skill library, and synthesize single-turn and multi-turn skill exploitation trajectories built around these skills with OpenCode. Using these data, we then train a hypernetwork that parameterizes both the skill content and the test-time exploitation methodology by receiving textual skills and converting them into LoRA adapters. Experimental results on six complex software engineering (SWE) subtasks demonstrate that, the proposed ParametricSkills averagely outperforms in-context learning by 6.44 points as judged by DeepSeek-V4-Flash, while also achieving significantly higher BERT Score and F1 score, confirming its effectiveness. Beyond performance, we further find that parametric skills, being inherently accumulative, offer a preliminary yet promising avenue toward test-time continual learning.

Problem

• skill acquisition
• instruction following
• long-context understanding
• textual skills
• large language models

Innovation

• ParametricSkills
• LoRA adapters
• hypernetwork
• test-time skill parameterization
• continual learning