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Stephen McAleese
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Software Engineer interested in AI and AI safety.

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3Stephen McAleese's Shortform
3y
14
Stephen McAleese's Shortform
Stephen McAleese6h40

I haven't heard anything about RULER on LessWrong yet:

RULER (Relative Universal LLM-Elicited Rewards) eliminates the need for hand-crafted reward functions by using an LLM-as-judge to automatically score agent trajectories. Simply define your task in the system prompt, and RULER handles the rest—no labeled data, expert feedback, or reward engineering required.

✨ Key Benefits:

  • 2-3x faster development - Skip reward function engineering entirely
  • General-purpose - Works across any task without modification
  • Strong performance - Matches or exceeds hand-crafted rewards in 3/4 benchmarks
  • Easy integration - Drop-in replacement for manual reward functions

Apparently it allows LLM agents to learn from experience and significantly improves reliability.

Link: https://github.com/OpenPipe/ART

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Summary of our Workshop on Post-AGI Outcomes
Stephen McAleese2d64

These talks are fascinating. Thanks for sharing.

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My current guess at the effect of AI automation on jobs
Stephen McAleese9d30

Great post, it explained some of the economics of job automation in simple terms and clarified my thinking on the subject which is not easy to do. This post has fewer upvotes than it should have.

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Daniel Kokotajlo's Shortform
Stephen McAleese2mo104

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.

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Foom & Doom 2: Technical alignment is hard
Stephen McAleese2moΩ350

Thank you for the reply!

Ok but I still feel somewhat more optimistic about reward learning working. Here are some reasons:

  • It's often the case that evaluation is easier than generation which would give the classifier an edge over the generator.
  • It's possible to make the classifier just as smart as the generator: this is already done in RLHF today: the generator is an LLM and the reward model is also based on an LLM.
  • It seems like there are quite a few examples of learned classifiers working well in practice:
    • It's hard to write spam that gets past an email spam classifier.
    • It's hard to jailbreak LLMs.
    • It's hard to write a bad paper that is accepted to a top ML conference or a bad blog post that gets lots of upvotes.

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.
 

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Foom & Doom 2: Technical alignment is hard
Stephen McAleese2mo*Ω350

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:

  1. The human programmers have a dataset of correct behaviors or a natural language description of what they want and they use this information to create a reward function or model automatically (e.g. Text2Reward).
  2. This learned reward model or generated code is used to train the policy.

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.

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Foom & Doom 1: “Brain in a box in a basement”
Stephen McAleese2mo42

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:

  • To the best of my knowledge, most academics and PhD students are expected to publish novel research in a highly autonomous way.
  • Novelists can work with a lot of autonomy when writing a book (though they're a minority).
  • There are also a lot of personal non-work goals like saving for retirement or raising kids which require high autonomy over a long period of time.
  • Small groups of people like a startup can work autonomously for years without going off the rails like a group of LLMs probably would after a while (e.g. the Claude bliss attractor).
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Foom & Doom 1: “Brain in a box in a basement”
Stephen McAleese2mo30

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.

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the void
Stephen McAleese2mo00

The post mentions Janus’s “Simulators” LessWrong blog post which was very popular in 2022 and received hundreds of upvotes.

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Mikhail Samin's Shortform
Stephen McAleese3mo20

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:

  • How would Anthropic fund their safety research if Claude is no longer SOTA and becomes less popular?
  • Is Anthropic supposed to learn from and test only models at current levels of capability and how does it learn about future advanced model behaviors? I haven’t heard a compelling argument for how we could solve superalignment by studying much less advanced models. Imagine trying to align GPT-4 or o3 by only studying and testing GPT-2 from 2019. In reality, future models will probably have lots of unknown unknowns and emergent properties that are difficult or impossible to predict in advance. And then there’s all the social consequences of AI like misuse which are difficult to predict in advance.

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.

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Road To AI Safety Excellence
3y
(+3/-2)
25Understanding LLMs: Insights from Mechanistic Interpretability
2d
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16How Can Average People Contribute to AI Safety?
6mo
4
195Shallow review of technical AI safety, 2024
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8mo
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23Geoffrey Hinton on the Past, Present, and Future of AI
11mo
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34Could We Automate AI Alignment Research?
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2y
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73An Overview of the AI Safety Funding Situation
2y
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26Retrospective on ‘GPT-4 Predictions’ After the Release of GPT-4
2y
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112GPT-4 Predictions
3y
27
3Stephen McAleese's Shortform
3y
14
8AGI as a Black Swan Event
3y
8
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