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Facebook Pushes Its Llama 4 AI Model to the Right, Wants to Present “Both Sides” [404 Media] (www.404media.co)

submitted 2 days ago by BlueMonday1984@awful.systems to c/techtakes@awful.systems

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[–] will_a113@lemmy.ml -2 points 1 day ago (2 children)

Do you have any sources on this? I started looking around for pre-training, training and post-training impact of new input but didn't find what I was looking for. In just my own experience with retraining (e.g. fine-tuning) pre-trained models, it seems to be pretty easy to add or remove data to get significantly different results than the original model.

[–] corbin@awful.systems 4 points 13 hours ago

It's well-known folklore that reinforcement learning with human feedback (RLHF), the standard post-training paradigm, reduces "alignment," the degree to which a pre-trained model has learned features of reality as it actually exists. Quoting from the abstract of the 2024 paper, Mitigating the Alignment Tax of RLHF (alternate link):

LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax.

[–] sailor_sega_saturn@awful.systems 5 points 14 hours ago

My go to source for the fact that LLM chatbots suck at writing reasoned replies is https://chatgpt.com/