Software Engineer interested in AI and AI safety.
Thank you for the reply!
Ok but I still feel somewhat more optimistic about reward learning working. Here are some reasons:
That said, from what I've read, researchers doing RL with verifiable rewards with LLMs (e.g. see the DeepSeek R1 paper) have only had success so far with rule-based rewards rather than learned reward functions. Quote from the DeepSeek R1 paper:
We do not apply the outcome or process neural reward model in developing DeepSeek-R1-Zero, because we find that the neural reward model may suffer from reward hacking in the large-scale reinforcement learning process, and retraining the reward model needs additional training resources and it complicates the whole training pipeline.
So I think we'll have to wait and see if people can successfully train LLMs to solve hard problems using learned RL reward functions in a way similar to RL with verifiable rewards.
In the post you say that human programmers will write the AI's reward function and there will be one step of indirection (and that the focus is the outer alignment problem).
But it seems likely to me that programmers won't know what code to write for the reward function since it would be hard to encode complex human values. In Superintelligence, Nick Bostrom calls this manual approach "direct specification" of values and argues that it's naive. Instead, it seems likely to be that programmers will continue to use reward learning algorithms like RLHF where:
If this happens then I think the evolution analogy would apply where there is some outer optimizer like natural selection that is choosing the reward function and then the reward function is the inner objective that is shaping the AI's behavior directly.
Edit: see AGI will have learnt reward functions for an in-depth post on the subject.
I think it depends on the context. It's the norm for employees in companies to have managers though as @Steven Byrnes said, this is partially for motivational purposes since the incentives of employees are often not fully aligned with those of the company. So this example is arguably more of an alignment than a capability problem.
I can think of some other examples of humans acting in highly autonomous ways:
Excellent post, thank you for taking the time to articulate your ideas in a high-quality and detailed way. I think this is a fantastic addition to LessWrong and the Alignment Forum. It offers a novel perspective on AI risk and does so in a curious and truth-seeking manner that's aimed at genuinely understanding different viewpoints.
Here are a few thoughts on the content of the first post:
I like how it offers a radical perspective on AGI in terms of human intelligence and describes the definition in an intuitive way. This is necessary as increasingly AGI is being redefined as something like "whatever LLM comes out next year". I definitely found the post illuminating and resulted in a perspective shift because it described an important but neglected vision of how AGI might develop. It feels like the discourse around LLMs is sucking the oxygen out of the room, making it difficult to seriously consider alternative scenarios.
I think the basic idea in the post is that LLMs are built by applying an increasing amount of compute to transformers trained via self-supervised or imitation learning but LLMs will be replaced by a future brain-like paradigm that will need much less compute while being much more effective.
This is a surprising prediction because it seems to run counter to Rich Sutton's bitter lesson which observes that, historically, general methods that leverage computation (like search and learning) have ultimately proven more effective than those that rely on human-designed cleverness or domain knowledge. The post seems to predict a reversal of this long-standing trend (or I'm just misunderstanding the lesson), where a more complex, insight-driven architecture will win out over simply scaling the current simple ones.
On the other hand, there is an ongoing trend of algorithmic progress and increasing computational efficiency which could smoothly lead to the future described in this post (though the post seems to describe a more discontinuous break between current and future AI paradigms).
If the post's prediction comes true, then I think we might see a new "biological lesson": brain-like algorithms will replace deep learning which replaced GOFAI.
Anthropic’s responsible scaling policy does mention pausing scaling if the capabilities of their models exceeds their best safety methods:
“We have designed the ASL system to strike a balance between effectively targeting catastrophic risk and incentivising beneficial applications and safety progress. On the one hand, the ASL system implicitly requires us to temporarily pause training of more powerful models if our AI scaling outstrips our ability to comply with the necessary safety procedures. But it does so in a way that directly incentivizes us to solve the necessary safety issues as a way to unlock further scaling, and allows us to use the most powerful models from the previous ASL level as a tool for developing safety features for the next level.”
I think OP and others in the thread are wondering why Anthropic doesn’t stop scaling now given the risks. I think the reason why is that in practice doing so would create a lot of problems:
Although I’m skeptical that alignment can be solved without a lot of empirical work on frontier models I still think it would better if AI progress were slower.
Thanks for the guide, ARENA is fantastic and I highly recommend it for people interested in learning interpretability!
I'm currently working through the ARENA course now. I completely skipped week 0 because I've done similar content in other courses and university and I'm on section Week 1: Transformer Interpretability now. I'm studying part time so I'm hoping to get through most of the content in a few months.
Some of my thoughts on avoiding the intelligence curse or gradual disempowerment and ensure that humans stay relevant:
After spending some time chatting with Gemini I've learned that a standard model-based RL AGI would probably just be a reward maximizer by default rather than learning complex stable values:
The "goal-content integrity" argument (that an AI might choose not to wirehead to protect its learned task-specific values) requires the AI to be more than just a standard model-based RL agent. It would need:
- A model of its own values and how they can change.
- A meta-preference for keeping its current values stable, even if changing them could lead to more "reward" as defined by its immediate reward signal.
The values of humans seem to go beyond maximizing reward and include things like preserving personal identity, self-esteem and maintaining a connection between effort and reward which makes the reward button less appealing than it would be to a standard model-based RL AGI.
An alternative idea is to put annual quotas on GPU production. The oil and dairy industries already do this to control prices and the fishing industry does it to avoid overfishing.