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<!DOCTYPE html>
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<head>
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<meta name="description" content="HKU AIL research series on continual learning — project pages and papers.">
<title>HKU AIL — Research Series Continual Learning</title>
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<body class="series-landing">
<section class="hero is-light">
<div class="hero-body">
<div class="container is-max-desktop">
<h1 class="title is-2 series-title has-text-centered">
Never: Research Series on Continual Learning
</h1>
<p class="has-text-centered is-size-5 mt-2 mb-2">
<em>Never Forget & Never Stop Learning</em>
</p>
<p class="subtitle is-5 series-lead has-text-centered mt-4">
by <a href="https://agentic-intelligence-lab.org/" target="_blank" rel="noopener">Agentic Intelligence Lab, The University of Hong Kong</a>.
</p>
</div>
</div>
</section>
<section class="section pt-5">
<div class="container is-widescreen">
<div class="papers-layout">
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<p class="paper-timeline-label">Timeline</p>
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<div class="paper-list">
<article class="paper-entry" id="paper-2026-09" data-month="2026-09">
<div class="paper-card is-placeholder" role="note" aria-label="Placeholder for an upcoming paper">
<div class="paper-card-thumb">
<span class="paper-card-badge">Coming soon</span>
</div>
<div class="paper-card-content">
<div class="paper-card-meta">
<time class="paper-card-date" datetime="2026-09">September 2026</time>
<div class="paper-card-authors">
<span>Agentic Intelligence Lab, HKU</span>
</div>
</div>
<h2 class="paper-card-title">RoboNever: A Real-World Dataset for Continual Robot Learning</h2>
<p class="paper-card-abstract">A large-scale real-world dataset designed for continual robot learning, capturing diverse tasks and deployment conditions for lifelong policy improvement.</p>
</div>
</div>
</article>
<article class="paper-entry" id="paper-2026-05" data-month="2026-05">
<div class="paper-card">
<a class="paper-card-thumb" href="pages/continualvla.html">
<img src="static/images/continualvla_thumbnail.jpg" alt="ContinualVLA" width="800" height="450" loading="lazy">
</a>
<div class="paper-card-content">
<div class="paper-card-meta">
<time class="paper-card-date" datetime="2026-05">May 2026</time>
<div class="paper-card-authors">
<span>Jiarun Zhu</span>
<span>Yijun Hong</span>
<span>Xiaoquan Sun</span>
<span>Zetian Xu</span>
<span>Mingqi Yuan</span>
<span>Zhiyong Wang</span>
<span>Wenjun Zeng</span>
<span>Jiayu Chen</span>
</div>
</div>
<h2 class="paper-card-title"><a href="pages/continualvla.html">Can VLA Models Learn from Real-World Data Continually without Forgetting?</a></h2>
<p class="paper-card-abstract">The first empirical study of real-world continual VLA learning. We find VLA models suffer significant catastrophic forgetting on heterogeneous real-world tasks, and identify key implementation factors that govern the success of experience replay for mitigating forgetting.</p>
<div class="paper-card-actions">
<a class="paper-card-link" href="https://arxiv.org/abs/2605.26820" target="_blank" rel="noopener">Paper</a>
<a class="paper-card-link" href="pages/continualvla.html">Website</a>
<a class="paper-card-link" href="https://github.com/Agentic-Intelligence-Lab/ContinualVLA" target="_blank" rel="noopener">Code</a>
</div>
</div>
</div>
</article>
<article class="paper-entry" id="paper-2026-03" data-month="2026-03">
<div class="paper-card">
<a class="paper-card-thumb" href="pages/paper-4.html">
<img src="static/images/vit.png" alt="" width="800" height="450" loading="lazy">
</a>
<div class="paper-card-content">
<div class="paper-card-meta">
<time class="paper-card-date" datetime="2026-03">March 2026</time>
<div class="paper-card-authors">
<span>Caihao Sun</span>
<span>Mingqi Yuan</span>
<span>Shiyuan Wang</span>
<span>Jiayu Chen</span>
</div>
</div>
<h2 class="paper-card-title"><a href="">Vision Transformers that Never Stop Learning</a></h2>
<p class="paper-card-abstract">We explore continual learning for vision transformers so visual representations can adapt to new data streams without erasing knowledge acquired on earlier tasks.</p>
<div class="paper-card-actions">
<a class="paper-card-link" href="https://arxiv.org/pdf/2603.07787">Paper</a>
</div>
</div>
</div>
</article>
<article class="paper-entry" id="paper-2026-01" data-month="2026-01">
<div class="paper-card">
<a class="paper-card-thumb" href="continual_policy_distillation.html">
<img src="static/images/cpd.png" alt="" width="800" height="450" loading="lazy">
</a>
<div class="paper-card-content">
<div class="paper-card-meta">
<time class="paper-card-date" datetime="2026-01">January 2026</time>
<div class="paper-card-authors">
<span>Qijun He</span>
<span>Yuxuan Li</span>
<span>Mingqi Yuan</span>
<span>Xiaoquan Sun</span>
<span>Wentse Chen</span>
<span>Jeff Schneider</span>
<span>Jiayu Chen</span>
</div>
</div>
<h2 class="paper-card-title"><a href="continual_policy_distillation.html">Continual Policy Distillation from Distributed Reinforcement Learning Teachers</a></h2>
<p class="paper-card-abstract">We present a teacher–student framework that decouples continual RL into distributed single-task teacher training and continual distillation into a central generalist model, mitigating forgetting while scaling to sequential tasks.</p>
<div class="paper-card-actions">
<a class="paper-card-link" href="https://arxiv.org/abs/2601.22475" target="_blank" rel="noopener">Paper</a>
<a class="paper-card-link" href="continual_policy_distillation.html">Website</a>
<a class="paper-card-link" href="https://github.com/Agentic-Intelligence-Lab/Continual-Policy-Distillation-from-Distributed-Reinforcement-Learning-Teachers" target="_blank" rel="noopener">Code</a>
</div>
</div>
</div>
</article>
</div>
</div>
</div>
</section>
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