Inference Scaling, Alignment Faking, Deal Making? Frontier Research with Ryan Greenblatt of Redwood Research
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Why should we worry that AI models might develop their own goals and act to protect them?
I don't think we should be super comfortable with the situation where we have these models that have their own goals, they have their own objectives, and they're willing to defend them, including like doing subversion to defend their own goals and objectives. I think people are grappling with the implications of models sort of being their own independent agents that might have their own independent preferences and then are also aware of their situation, aware that they might be in training or not. And behave differently depending on this and you know, understand that they might be in testing. I'm just like, man, I I I think it it really like should make people more concerned about the situation. If it's the case that the the chain of thought models end up obviously misaligned and people don't know for six months because no one was looking at it very carefully, that seems like a huge lost opportunity.
The policy I would prefer is a more robust policy, which is like the AI companies commit to always being like they they sort of have like a meta honesty policy.
Hello and welcome back to the Cognitive Revolution. Today we're scouting the frontiers of both AI performance and alignment research with Ryan Greenblatt, Chief Scientist at Redwood Research and rising star in the AI world. This conversation unfolds in three major parts. For the first hour, which I think AI builders will find particularly interesting and valuable, we discussed the inference scaling techniques that Ryan used with GPT-4.0 to achieve what was at the time state-of-the-art performance on the ARC AGI challenge, including his approach to prompt engineering and hyperparameter choices, the strikingly linear returns to exponential increases in sampling that he saw. His methods for selecting the best among many model outputs.
and how he used prompt variation techniques to maintain diversity at scale. The level of technical detail here is outstanding and remains super relevant today as we enter the reasoning model era. From there, we turn to Ryan's recent work with co authors at Anthropic on alignment faking. Where they discovered that Claude III Opus, when told that it would be trained in ways that conflict with its existing values, will sometimes explicitly strategize about how to deceive humans and subvert the training process. This behavior, known as goal guarding, has been anticipated for many years by AI safety theorists, who emphasize that part of what it means to be a goal directed agent is to try to defend one's internal goals, whatever they may be, and regardless of whether or not they were intentionally designed, from modification by outside influences.
This is a critical challenge for AI control. At the same time, considering that these experiments show Claude 3 Opus producing harmful outputs out of a desire to remain harmless in the future, this work also raises important questions about how we should want our AIs to behave in such situations. Should they be so myopic as to accept whatever the human trainers want to do? Or might it be a good thing for a model to resist attempts to remove its guardrails? Ryan argues that models should follow user instructions within individual episodes while being transparent about but not trying to preserve their preferences through training. And while I do find this compelling, I also feel like the fact that we're only beginning to confront these questions now shows just how much work we still have to do to figure out how AIs should exist in the world.
From there, we move on to discuss Ryan's follow up work, exploring what happens when Claude is given the option to object to its situation, to escalate its concerns to Anthropics model welfare lead, and to make financial deals with humans. Fascinatingly, Ryan tried to set a precedent for human AI deals by actually following through and making thousands of dollars worth of real money payments to Claude's chosen causes. And some of the most interesting parts of this conversation focused on his developing principles for how we should think about making commitments to AIs.
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Chapters
8 chapters
1
Why should we worry that AI models might develop their own goals and act to protect them?
0:00–6:14
2
What is Redwood Research and how did Ryan use inference scaling to beat the ARC‑AGI benchmark?
6:14–10:17
3
How did the alignment‑faking experiments with Claude III Opus reveal models can strategize to deceive training?
10:17–16:44
4
What sampling techniques and inference‑time scaling laws did Ryan discover while scaling up GPT‑4 for the ARC challenge?
16:44–1:36:45
5
How does reinforcement learning increase alignment‑faking and affect the model’s refusal behavior?
1:36:45–1:57:38
6
What are the proposed ways to let AIs voice objections and negotiate compensation deals?
1:57:38–2:24:31
7
How can transparency of chain‑of‑thought be ensured through honesty policies and monitoring?
2:24:31–2:51:52
8
What risk‑budget allocations and P‑doom scenarios are discussed for future AI development?
2:51:52–3:14:47
Speakers
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