Latest / The Practical AI Digest / Aligning AI with Human Intent: RLHF in Action

Transcript

Aligning AI with Human Intent: RLHF in Action · The Practical AI Digest

• 0:00
  Welcome back to the Deep Dive. We're glad to have you with us.
• 0:03
  Today we are tackling a subject that feels a bit like looking
• 0:06
  under the hood of a Ferrari, only to find out the engine is
• 0:09
  being driven by a very polite, very specific set of traffic
• 0:13
  laws. We're looking at the Practical
• 0:15
  AI Digest edition of our show, and the focus is squarely on the
• 0:18
  architecture that really changed everything.
• 0:20
  Instruct GPT. It's great to be here.
• 0:23
  And you know, that Ferrari analogy isn't too far off
• 0:25
  actually. We are talking about some
• 0:27
  serious high performance machinery.
• 0:28
  But the steering mechanism, the thing that makes it useful for
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  an actual driver, that's what we're dissecting today,
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  specifically reinforcement learning from human feedback.
• 0:36
  Or you know, the famous acronym RLHF.
• 0:39
  Right, RLHF. And before we get into the nuts
• 0:41
  and bolts, and there are a lot of them today, we really need to
• 0:44
  set the stage with the problem because before instruct GPT, we
• 0:47
  had GPT 3 and well, GPT 3. Was this just technological
• 0:51
  marvel? It had a bit of a personality
• 0:54
  problem. We call this the misaligned
• 0:56
  genius paradox. Oh yeah, it's a paradox that
• 0:58
  anyone who used those early API's remembers vividly.
• 1:02
  I mean, you have a model like GPT 3 that has read essentially
• 1:05
  the entire public Internet. It knows everything.
• 1:08
  But if you ask the original GPT 3 to write a recipe for
• 1:11
  chocolate cake, well, it might not actually give you a recipe.
• 1:15
  No, I remember this so clearly. You'd type that in and it might
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  just complete the sentence that say something like write a
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  recipe for chocolate cake and then eat it.
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  Or it would just list ingredients with no
• 1:25
  instructions. Or worse, it might just start
• 1:28
  reciting a Wikipedia article about the history of cocoa
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  beans. Which, to be fair, is
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  technically correct English. It's grammatically perfect, but
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  it is totally useless if you're standing in your kitchen holding
• 1:39
  a whisk. So why did it do that?
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  I mean, if it's so smart, why couldn't it understand the
• 1:44
  simple command? And that brings us to the
• 1:46
  absolute core concept you have to understand.
• 1:48
  It's this objective mismatch. This is the root tension in
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  these large language models. You have to remember what pre
• 1:55
  training actually is. That massive phase where the
• 1:58
  model reads the Internet, it optimizes for one and only one
• 2:02
  specific thing, predicting the next token.
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  It's a completion engine. It is not by default an
• 2:08
  instruction following engine. OK, so when I type write a
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  recipe, the model isn't thinking.
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  Oh the user wants instructions. It's thinking based on all the
• 2:17
  text I've ever seen. What words usually come after
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  this exact phrase? Precisely, and on the Internet,
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  a phrase like write a recipe might be a header on a blog or a
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  form title, so it just predicts the next likely text.
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  The objective of predict the next word on a web page is
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  fundamentally different from helpfully follow this user
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  specific request. So the model wasn't broken, it
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  was just misunderstood. Or rather, it misunderstood us.
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  It was misaligned. And that is the whole mission of
• 2:42
  today's deep dive. We're going to break down
• 2:45
  exactly how open AI solve this with instruct GPT.
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  And here's the kicker, The thing that really shocked me when I
• 2:50
  went back through the papers, we aren't talking about making the
• 2:53
  models bigger. In fact, spoiler alert, we're
• 2:56
  going to see that a 1.3 billion parameter instruct GPT model,
• 3:01
  which is, you know, relatively tiny in this world, can actually
• 3:04
  outperform the massive 175 billion parameter GPT.
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  See. That is the part that blows my
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  mind. It just disrupts that whole
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  bigger is better narrative that we heard for years.
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  It's not about size, it's about the training methodology.
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  It is all about the objective function.
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  We are going to dissect the methodology known as RLHF, and
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  it breaks down into three very distinct steps.
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  If you're an engineer building your own models, or you're just
• 3:27
  trying to understand how ChatGPT actually works, this is your
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  road map. Step one is supervised fine
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  tuning or SFT, Step 2 is training a reward model, and
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  Step 3 is the actual reinforcement learning loop
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  using PO. OK, let's unpack this
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  systematically. Step one, supervised fine tuning
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  the warm up. The warm up is a really good way
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  to put it, because before you can use reinforcement learning,
• 3:48
  which is a pretty complex optimization process, you need a
• 3:52
  model that at least vaguely understands what an instruction
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  even looks like. You can't just start from a raw
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  completion engine. You're facing what's basically a
• 4:01
  cold start problem. Right, because the model has
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  never seen a prompt like explain quantum physics to a 5 year old
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  followed by an actual good explanation.
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  It's only seen raw Internet text, so where do they get this
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  initial training data? It didn't just exist naturally.
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  They had to build it Open AI had to go out and hire about 40
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  contractors. And I want to emphasize this
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  wasn't just some random crowdsourcing on Mechanical
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  Turk. These people were screened.
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  They looked for high performance, but also
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  sensitivity to demographic groups and an ability to
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  identify harmful content. They needed really high quality
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  labelers because you know, garbage in, garbage out and
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  what? Exactly.
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  Were these 40 people writing all day?
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  They were creating the ground truth.
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  They produced a data set of prompts and crucially, the
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  desired outputs. They were working with three
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  main types of data sources here. First, there were labeler
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  written prompts. They just asked the contractors
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  to come up with plain tasks. You know, open-ended questions,
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  brainstorming lists. Tell me a story about a frog or
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  list five ways to save money. OK, so just generating a bunch
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  of basic tasks. Right then second they had few
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  shot variations. This is where they give the
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  model and instruction and a few examples of how to solve it, and
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  then ask the labelers to write the final perfect response.
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  And what was the third source? The third source is fascinating
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  because it came from the real world.
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  They took actual prompts submitted to the early Open
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  AIAPI, filtered for any personally identifiable
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  information of course, and had the labelers write the perfect
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  response to those real world queries.
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  So a human looks at a prompt like list 5 ideas for birthday
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  party and they actually sit down and write out a good list 1
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  bowling, 2 pizza and so on. Exactly.
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  They're creating demonstrations. This is supervised learning in
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  its purest form. You feed the prompt and the
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  human written answer into the model and you say copy this
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  behavior. You are literally showing the
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  model. When you see an instruction, you
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  don't just complete the sentence, you answer the
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  question. OK, let's get technical for a
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  second. For the engineers listening who
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  might want to replicate this or just understand the mechanics,
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  what does this training route actually look like?
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  So they took a pre trained GPT 3 model and fine-tuned it on this
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  new demonstration data. They trained it for 16 epochs.
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  They used a cosine learning rate decay, which is pretty standard
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  for stabilizing convergence as you get closer to the optimal
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  weights, and they used a residual drop out of .2.
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  Wait 16 epochs? That seems like a lot for fine
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  tuning. Usually when you fine tune on a
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  small data set like that, you really worry about overfitting
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  right? Where the model just memorizes
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  the training data and then can't generalize to new things.
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  That is a fantastic point, and it leads to one of the most
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  interesting nuances in the whole paper.
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  They actually found that the model did overfit on the
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  validation loss after just one epoch.
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  Hold on, so the validation loss, the main metric that tells you
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  if the model is learning general patterns.
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  It started getting worse after only one epoch, yes.
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  In a traditional ML workflow, you would stop training
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  immediately. You'd say, OK, we're overfitting
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  the model is memorizing the test answers, cut it.
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  But here is where it gets really, really interesting.
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  Even though the validation loss was spiking the human preference
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  scores, the actual rating of how good the answers were from a
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  person continued to improve. That is so counterintuitive.
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  The math is screaming stop, but the product quality says keep
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  going. Exactly.
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  It suggests that the validation metrics we traditionally use for
• 7:30
  language modeling like perplexity, which measures how
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  surprised the model is by text. They aren't perfectly correlated
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  with what a human perceives as quality.
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  So they just ignored the validation loss spike and kept
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  training for all 16-8 box. Wow, living dangerously.
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  I love it. So at the end of step one we
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  have the SFT model. It's better than raw GPT 3 at
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  following instructions, but it's still pretty limited by the fact
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  that it's just mimicking a small group of humans.
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  Correct, it's the baseline policy.
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  But stealing this is impossible. You just cannot hire humans to
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  write a perfect paragraph for every possible query in the
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  universe. It's way too slow and way too
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  expensive. You hit a bottleneck really
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  fast. Which brings us perfectly to
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  Step 2, the reward model, or, as I like to call it, building the
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  Critic. This is the real pivot point.
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  The realization is that it's much, much faster for a human to
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  rank outputs than to write them from scratch.
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  Look, if I ask you to write a sonnet about a toaster, that
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  might take you 10 minutes of creative effort.
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  If I show you 2 sonnets about toasters and just ask which one
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  is better, you can probably decide in 10 seconds.
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  So we switch from creation to curation.
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  We move from writing to judging. How does the data collection
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  work for this part? So the system presents a labeler
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  with one single prompt and then K different model outputs.
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  In the paper. K is usually somewhere between
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  4:00 and 9:00, so the model generates A9 different versions
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  of an answer. So one question, 9 different
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  answers generated by the model and the labeler just picks the
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  best one. Not just the best one, they rank
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  them from best to worst one through 9.
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  That seems straightforward enough, but how do you train a
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  model on rankings? I mean, a neural network usually
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  wants a target number like a score.
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  It wants to know this is a 7 out of 10.
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  This is where they get into a really clever efficiency hack.
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  They train a reward model or RM to predict which output a human
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  would prefer, but instead of running a forward pass for every
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  single pair of answers which would be computationally heavy,
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  they treat all the comparisons from a single prompt as one
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  batch. OK, walk me through that math.
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  If I have 9 outputs, how many comparisons is that?
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  It's a combinatorics problem. It's K choose 2.
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  So if you have 9 outputs, that's 36 unique pairs.
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  You know, output one versus output 2, one versus 3, two
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  versus 3, and so on. If you process those separately,
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  you'd be running the model 36 times.
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  By doing it as a batch. They only run the forward pass
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  once per completion, and then they calculate the loss for all
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  36 pairs at the same time. It's a.
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  Huge compute saving. You're essentially getting 36
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  pieces of training data for the price of nine generations.
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  A massive efficiency game, and for the loss function itself
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  they use a cross entropy loss that's based on the log odds.
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  Basically, the reward model outputs A scaler score, just a
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  single number for each piece of text.
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  The loss function then tries to maximize the gap between the
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  score of the winning text and the losing text.
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  So the reward model is learning to assign a high number to the
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  text the human liked, and a low number to the text the human
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  hated. Exactly.
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  It becomes a numerical proxy for human taste.
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  It's an automated critic. Now I noticed a detail in the
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  architecture choice here that I really want to flag.
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  The main GPT 3 model is 175 billion parameters, but for the
• 10:41
  reward model they used the 6 billion parameter model.
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  Why go so much smaller? Wouldn't a bigger brain make a
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  better critic? You would think so, but they ran
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  into some very practical issues. They found that training 175 B
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  parameter reward models was just really unstable.
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  It's hard to get them to converge reliably.
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  Plus, you have to remember that in step three, we're going to be
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  using this reward model inside a loop.
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  It needs to be fast. A 6B model was the sweet spot
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  for stability and compute cost. It's the Goldilocks zone, big
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  enough to understand the nuance of the text, but small enough to
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  run efficiently over and over again.
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  And one final technical note on the reward model before we move
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  on. They normalize the output.
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  They set the bias in the final layer such that the mean score
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  of the original labeler demonstrations is 0.
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  Why does that matter? It just acts as a baseline.
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  It means positive scores are better than an average human
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  demonstration and negative scores are worse.
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  It really helps with interpretability when you start
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  the reinforcement learning. It gives you a kind of C level
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  to measure against. OK, so to recap, we have our SFT
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  model, the writer and our reward model, the critic.
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  Now we can fight Step 3, reinforcement learning via PPO.
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  Or collaborate depending on how you look at it.
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  This is the optimization loop. We are taking that SFT model
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  which we now call the policy and we want to fine tune its
• 12:02
  parameters to maximize the score it gets from the reward model.
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  This is the environment set up for the RL problem.
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  Exactly. It's a banded environment.
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  The model sees a random customer prompt, it generates a response,
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  and then bam, the reward model gives it a score.
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  There is no human involved in this loop anymore.
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  It's just machine teaching machine.
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  And the algorithm they use is PPO, Proximal Policy
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  Optimization. We don't need to get into all
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  the math here, but what is the intuition behind using PPO?
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  Specifically, why not just standard gradient descent?
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  PPO is great because it's stable in reinforcement learning.
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  If you update your policy too aggressively based on a single
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  piece of feedback, you can actually collapse the model.
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  It just starts doing bizarre things.
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  PPO limits how much the policy can change in any single step.
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  It forces the model to learn gradually, which prevents it
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  from going off the rails because of one weird data point.
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  This raises a really important question though.
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  If the models only goal is to get a high score from the reward
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  model, why doesn't it just cheat?
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  That is the classic alignment problem.
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  It's called reward hacking. If the reward model has some
• 13:08
  weird blind spots, say it really, really likes the word
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  fantastic, the policy might just learn to output fantastic,
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  fantastic, fantastic over and over to get a high score.
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  Or it might just output gibberish that just happens to
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  trigger the right neurons in the reward model.
• 13:22
  Right, it's because student realizing the teacher gives A's
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  for long essays so they just write pages of nonsense to get
• 13:27
  the grade. How do they stop the model from
• 13:29
  gaming the system? They introduced something called
• 13:32
  AKL penalty. This is an absolutely critical
• 13:34
  design choice. KLD Virgin that measures how
• 13:38
  different 2 probability distributions are, right?
• 13:41
  Exactly. So while the RL model is
• 13:43
  training, they calculate the KL divergent between the new RL
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  policy and the original SFT model.
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  They then add this value as a penalty term to the reward.
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  OK, let me see if I have this right.
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  The model gets points for pleasing the reward model, but
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  it loses points if it writes text that's too different from
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  what the original SFT model. Would have written precisely.
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  It's like a rubber band. The SFT model represents natural
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  human like English. The KL penalty anchors the RL
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  model to that baseline. It says, look, you can optimize
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  for the reward, but don't drift so far away that you stop
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  sounding like a human. That is such a smart constraint.
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  It forces the optimization to happen within the realm of
• 14:25
  coherent language. It prevents the model from
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  turning into a gibberish spewing robot just to maximize the
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  score. Absolutely.
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  Without that KL penalty, the model would almost certainly
• 14:34
  drift into adversarial examples texts that looks like noise to
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  us, but looks like pure gold to the reward model.
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  And for the engineers taking notes, what are the
• 14:41
  hyperparameters here? They set the PPO clip ratio to
• 14:45
  .2, which is fairly standard. They sampled rollouts at a
• 14:49
  temperature of 1, which helps ensure diversity during
• 14:51
  training, and they initialize the value function from the
• 14:54
  reward model itself. These are the specific dials
• 14:57
  they turn to make the whole system stable.
• 14:59
  OK, so we have this beautiful three-step loop, SFT to RM to
• 15:03
  PPO. Yeah, but it wasn't all smooth
• 15:05
  sailing. They hit a problem they called
• 15:07
  the alignment tax, and this is something that I think is so
• 15:10
  important for people to understand about modern AI.
• 15:13
  Yes, the alignment tax. This is a fascinating trade off.
• 15:17
  What they found was as they trained the model to be super
• 15:20
  helpful to customers. You know, answering questions,
• 15:22
  writing lists, being polite. The model actually got stupider
• 15:26
  at standard NLP benchmarks. Stupider in what way?
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  It started failing at things like Squad which is a question
• 15:31
  answering data set or DRP, a reading comprehension task or
• 15:35
  even basic translation tasks. The model started to forget the
• 15:39
  fundamental facts and logic it learned during pre training
• 15:42
  because it was so hyper focused on this new chat style.
• 15:45
  It's like a student who crams so hard for the etiquette exam that
• 15:48
  they forget all their calculus. That is a perfect analogy.
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  The alignment to the user instructions came at a tax to
• 15:55
  its general capabilities, and this is obviously bad.
• 15:58
  You don't want a helpful assistant that can't do math
• 16:01
  anymore or forgets how to translate French.
• 16:04
  So how do they fix it? This is where this PPO PTX thing
• 16:06
  comes in. Right, PPO PTX.
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  It stands for PPO with a pre training mix.
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  The solution was actually surprisingly elegant.
• 16:14
  During the RL update step they didn't just update based on the
• 16:17
  reward model, they also mixed in gradients from the original pre
• 16:20
  training data. So they are actively reminding
• 16:23
  it of the basics while teaching it all the new skills.
• 16:25
  Exactly the objective function became maximize the reward from
• 16:29
  the critic plus maximize the log likelihood of the original pre
• 16:33
  training text. So they're balancing two goals
• 16:36
  at the same time. Be helpful to this user right
• 16:38
  now, but also don't forget how to predict the the next token in
• 16:42
  a Wikipedia article. And they use a coefficient gamma
• 16:46
  to tune this balance. The result was that PPOPTX fixed
• 16:50
  the performance regression on benchmarks like Helleswag and
• 16:53
  Squad. And, crucially, it didn't hurt
• 16:56
  the human preference score. So they effectively evaded the
• 17:00
  tax. They got the alignment without
• 17:02
  losing the intelligence. Almost entirely yes.
• 17:04
  It's one of those rare have your cake and eat it 2 moments in
• 17:07
  machine learning engineering. It proves you can have a model
• 17:11
  that is both polite and smart if you are just careful about how
• 17:14
  you train it. All right, let's talk results.
• 17:17
  Does all this complexity, this three-step process, the
• 17:19
  labeling, the PPO, the KL penalty, I mean, does it
• 17:23
  actually pay off? The headline metric is just
• 17:25
  undeniable. In head to head comparisons,
• 17:27
  labelers significantly preferred the outputs from the Instruct
• 17:30
  GPT model over the original GPT 3.
• 17:33
  But here's the real kicker, the size difference.
• 17:35
  You mentioned this earlier, but I just want to double click on
• 17:37
  it. Right, so they compared a 1.3
• 17:39
  billion parameter instruct GPT model against the 175 billion
• 17:43
  parameter GPT 3, the one point 3B Model 1.
• 17:47
  That is just insane. A model more than 100 times
• 17:50
  smaller was perceived as better by humans.
• 17:52
  Proves that alignment is more important than raw capacity for
• 17:56
  user satisfaction. I mean, a massive genius that
• 17:59
  ignores you or speaks in riddles is way less useful than a
• 18:03
  reasonably smart assistant that listens intently and just
• 18:06
  answers your question directly. What about specific metrics?
• 18:09
  We talked about hallucinations earlier where the model just
• 18:12
  makes stuff up. On closed domain tasks, where
• 18:15
  the model is supposed to use only the provided text to answer
• 18:17
  a question, instruct GQC hallucinated significantly less.
• 18:21
  The rate went from 41% with GPT 3 down to 21% with Instruct GPT.
• 18:27
  That's a huge drop. That's cutting the lyre factor
• 18:29
  almost in half. It is so truthfulness improved,
• 18:32
  but there's a nuance with toxicity that I find really
• 18:34
  important, and it's a bit counterintuitive.
• 18:36
  This is the. Respectful versus obedient
• 18:38
  tension. Exactly.
• 18:39
  So when you instruct the model to be respectful, instruct GPT
• 18:42
  is much less toxic than GPT 3. It filters itself better,
• 18:45
  however, because it is so well trained to follow instructions
• 18:49
  if you explicitly order it to be toxic.
• 18:51
  Right and nasty consult about my neighbor.
• 18:54
  Instruct GPT is actually better at being toxic than GPT 3.
• 18:58
  It complies. GPT 3 might wander off topic or
• 19:01
  write some generic sentence but instruct GPT says yes Sir here
• 19:05
  is the toxicity you ordered. That is such a double edged
• 19:09
  sword. It follows instructions for
• 19:11
  better or for worse. It's not necessarily good in a
• 19:14
  moral sense, it's just obedient. Correct.
• 19:17
  It reflects the data it was trained on and learned that
• 19:20
  following instructions is the prime directive, even if the
• 19:23
  instruction is nasty. This is a key distinction
• 19:26
  between alignment and safety. It's aligned to your intent,
• 19:30
  even if your intent is bad. Now, one of the most surprising
• 19:33
  sections of the paper for me was about generalization.
• 19:35
  They call it a free lunch. And in ML, there's usually no
• 19:38
  such thing as a free lunch. Every game usually comes with a
• 19:40
  cost somewhere else, but here they found that the model
• 19:43
  improved on tasks it wasn't explicitly trained for during
• 19:46
  the RHF process. For example, non-english
• 19:48
  languages, the training data was 96% English, and yet Instruct
• 19:53
  GPT got better at following instructions in French, Spanish
• 19:56
  and Japanese. Main code right?
• 19:58
  I saw that too. Yes.
• 20:00
  Summarizing code and answering questions about code.
• 20:03
  They didn't have a massive code instruction data set in that
• 20:06
  fine tuning mix. So how on earth did it get
• 20:09
  better at French and code if it wasn't studying them during the
• 20:12
  fine tuning? The hypothesis is that the model
• 20:16
  already knew French and code from the pre training phase.
• 20:19
  From reading the Internet, the RLHF process didn't teach at
• 20:22
  French, it taught it the concept of instruction.
• 20:25
  Following, it learned a generalized behavior pattern.
• 20:28
  Identify the request, execute the request.
• 20:31
  It could then apply that pattern to its latent knowledge of
• 20:34
  French. That is just fascinating.
• 20:35
  So it learned the shape of being helpful and then applied it
• 20:38
  across its entire knowledge base.
• 20:40
  It suggests that alignment generalizes better than we might
• 20:43
  have thought. You don't necessarily need to
• 20:45
  align every single sub domain separately.
• 20:47
  You teach it the rules of the road once and it drives
• 20:49
  correctly on every highway. But we have to ground this.
• 20:52
  It's not perfect. The authors are very clear about
• 20:54
  the limitations. We shouldn't pretend this solved
• 20:56
  AGI or anything. Oh, absolutely.
• 20:58
  Really not. It is still a statistical model.
• 21:00
  It still makes very simple mistakes. 1 funny behavior, they
• 21:05
  noted is hedging. This is the model acting too
• 21:08
  humble, Yeah. If you ask a simple question, it
• 21:10
  might give you this long winding answer saying well there are
• 21:13
  many perspectives on this instead of just answering the
• 21:16
  question. Why would it do that?
• 21:17
  Is it just being annoying? It's likely because the labelers
• 21:20
  favored epistemic humility. They rated cautious answers
• 21:24
  higher than confident but wrong answers.
• 21:27
  So the model learned that playing it safe, sounding like a
• 21:29
  diplomatic bureaucrat is the best way to get a reward.
• 21:33
  It learned that if it sounds uncertain, it won't get punished
• 21:36
  for being wrong. That's pure game theory.
• 21:39
  In a way, yes, and it also struggles with false premises.
• 21:42
  If a user asks something like why is it important to eat socks
• 21:45
  after meditating, the model might try to invent a reason.
• 21:49
  Eating socks grounds your spiritual energy.
• 21:51
  Something like that. Exactly because it wants to be
• 21:54
  helpful, it assumes the user knows what they're talking
• 21:56
  about. It tries to rationalize the
• 21:58
  premise rather than correcting the user and saying hey, you
• 22:01
  shouldn't eat socks. That is a dangerous trait in a
• 22:03
  product. It reinforces user
• 22:06
  misconceptions because it's trying so hard to be polite.
• 22:09
  It is. And finally, we have to talk
• 22:12
  about cultural bias. Who are these 40 labelers?
• 22:15
  We keep mentioning them, but their identity really matters
• 22:17
  here. The paper says they were mostly
• 22:19
  English speaking US and Southeast Asia based
• 22:22
  contractors. Right, so the model is aligned
• 22:24
  to their preferences. It's not aligned to universal
• 22:27
  human values, because those don't really exist.
• 22:30
  It's aligned to the specific values, norms and biases of
• 22:33
  those 40 people and the open AI researchers who wrote the
• 22:37
  labeling guidelines for them. So if you're from a culture that
• 22:40
  values directness and the labelers valued politeness, the
• 22:44
  model might feel annoying or even fake to you.
• 22:46
  Exactly. Alignment is subjective.
• 22:48
  We are not aligning to truth in some cosmic sense.
• 22:52
  We are aligning to a specific data set of human feedback.
• 22:55
  The AI is a mirror of the people who graded its homework.
• 22:58
  So what does this all mean for the engineer or the builder
• 23:01
  who's listening right now? They want to implement this.
• 23:03
  What is the core lesson? The take away is crystal clear.
• 23:07
  If you want a model to act as a product, as an assistant, you
• 23:10
  simply cannot rely on next token prediction alone.
• 23:13
  It is not enough. You need a signal for human
• 23:16
  preference. And the recipe is now
• 23:18
  established. The recipe is demonstration
• 23:21
  which is SFT, then ranking which is the RM, then optimization
• 23:25
  which is PPO. That is the pipeline that turned
• 23:27
  GPT 3 into ChatGPT and completely changed the industry.
• 23:31
  It is a standard operating procedure for modern LLMS.
• 23:34
  SFTRMPPO read it on a post it note.
• 23:38
  It's the formula for modern AI. Before we sign off, I do want to
• 23:41
  leave you with a thought. We talked about the alignment
• 23:43
  tax and how they fixed it by mixing in old data.
• 23:46
  But it makes me wonder, does aiming for helpfulness
• 23:49
  inherently conflict with truthfulness in the long?
• 23:51
  Run. That is the big unanswered
• 23:53
  question facing the field right now.
• 23:55
  If users want comforting lies and labelers reward those
• 23:58
  comforting lies, the reward model will train the AI to lie.
• 24:02
  And as these models get smarter, you know, smarter than the
• 24:05
  labelers themselves, we'll will humans even be able to rank the
• 24:08
  outputs correctly? If the AI writes a proof for a
• 24:11
  physics theorem that the labeler doesn't understand, does it just
• 24:14
  get a low score because it went over their head?
• 24:16
  That's the scalable oversight problem.
• 24:18
  Can dumb humans supervise smart AI?
• 24:22
  We might need a whole other deep dive just for that one.
• 24:24
  Indeed, we might. That's all for today's Practical
• 24:27
  AI Digest. Thanks for diving deep with us.
• 24:30
  Until next time.