# Clare Continual Learning For Vision Language

> To teach robots complex manipulation tasks, it is now a common practice to fine-tune a pre-trained vision-language-action model (VLA) on task-specific data. However, since this recipe updates existing representations, it is unsuitable for long-term operation in the real world, where robots must continually adapt to new tasks and environments while retaining the knowledge they have already acquired. Existing continual learning methods for robotics commonly require storing previous data (exemplars...

- Skill: `adu2021/clare-continual-learning-for-vision-language` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/clare-continual-learning-for-vision-language`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/clare-continual-learning-for-vision-language/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/clare-continual-learning-for-vision-language

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## Problem

CLARE addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.

## Key Approach

The paper introduces a novel framework, methodology, or benchmark for clare. The core contributions include:

1. Systematic framework or benchmark for agent evaluation and development
2. Empirical findings on agent performance, efficiency, or capabilities  
3. Generalizable principles applicable across domains

## When to Use

Use this skill when you need to:
- Evaluate or benchmark autonomous agent systems
- Understand best practices in agent design and evaluation
- Learn empirical results on agent performance
- Improve agent efficiency, reasoning, or capabilities

## When NOT to Use

- For non-agent-related tasks
- When seeking quick implementation code (see the paper for details)
- For general knowledge unrelated to autonomous agents

## Resources

- ArXiv Abstract: https://arxiv.org/abs/2601.09512
- Full PDF: https://arxiv.org/pdf/2601.09512
- HTML Version: https://arxiv.org/html/2601.09512

See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.