Summary
This research investigates the brittleness of safeguards in Large Language Models (LLMs) that allow them to generate harmful content despite alignment training. Current protections are easily bypassed by jailbreaks and fine-tuning, leading to 'emergent misalignment.' By employing targeted weight pruning, researchers aim to uncover the internal organization and mechanisms of harmfulness within LLMs, providing insights for more robust safety measures.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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