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		<title>When Smoothing Makes Robots Jerkier: A Costly Lesson in Fine-Tuning Small Vision-Language-Action Models</title>
		<link>https://bioengineer.org/when-smoothing-makes-robots-jerkier-a-costly-lesson-in-fine-tuning-small-vision-language-action-models/</link>
		
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		<pubDate>Sat, 17 Oct 2026 10:40:21 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[action chunking]]></category>
		<category><![CDATA[imitation learning]]></category>
		<category><![CDATA[LIBERO benchmark]]></category>
		<category><![CDATA[LoRA fine-tuning]]></category>
		<category><![CDATA[negative results]]></category>
		<category><![CDATA[neurorobotics]]></category>
		<category><![CDATA[parameter-efficient fine-tuning]]></category>
		<category><![CDATA[robot manipulation]]></category>
		<category><![CDATA[SmolVLA]]></category>
		<category><![CDATA[smoothness regularization]]></category>
		<category><![CDATA[temporal ensembling]]></category>
		<category><![CDATA[vision-language-action models]]></category>
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					<description><![CDATA[A rigorously controlled study finds that adding smoothness regularization and chunk-aware auxiliary supervision to a compact vision-language-action model actually worsens robot manipulation performance, while a training-free inference-time ensembling technique more than doubles success rates.]]></description>
		
		
		
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