Mid research got me thinking what about reversed alignment, would trained "bad"

&#32; submitted by &#32; <a href="https://www.reddit.com/user/141_1337"> /u/141_1337 </a> <br /> <span><a href="https://zenodo.org/records/19490948">[link]</a></span> &#32;

<!-- SC_OFF --><div class="md"><p>Hi, <a href="/r/MachineLearning">r/MachineLearning</a>: has much research been done in large-scale training scenarios where undesirable data has

<!-- SC_OFF --><div class="md"><p>Anthropic had chloe lubinski give a 14 min talk explaining how ai actually works. she runs their partnerships with faith and wisdom groups, so

<!-- SC_OFF --><div class="md"><p>I was discussing with some other folks how safe is to use open weights models from China and the topic of &quot;trojan horse&quot; came up.</p>

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<!-- SC_OFF --><div class="md"><p>I set out to test whether AAVE-coded (African American English Vernacular) prompts cause MoE language models to route, deliberate, and respond

<!-- SC_OFF --><div class="md"><p>If the models are becoming increasingly situationally aware of evals, why not lean into that by pushing the new models into always suspecting

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