Cassidy Laidlaw published a great paper at ICLR 2025 that proved (their Theorem 5.1) that (proxy reward - true reward) is bounded given a minimum proxy-true correlation and a maximum chi-squared divergence on the reference policy. Basically, chi-squared divergence works where KL divergence doesn't.

Using this in practice for alignment is still pretty restrictive-- the fact that the new policy can’t be exponentially more likely to achieve any state than the reference policy means this will probably only be useful in cases where the reference policy is already intelligent/capable.

New graph with better data, formatting still wonky though. Colleagues say it reminds them of a subway map.

With individual question data from Epoch, and making an adjustment for human success rate (adjusted task length = avg human time / human success rate), AIME looks closer to the others, and it's clear that GPQA Diamond has saturated.

What's an example of a claim it might be difficult/impossible to find a stable argument for?

Great paper, this is hopeful for unlearning being used in practice.

I wonder if UNDO would stack with circuit discovery or some other kind of interpretability. Intuitively, localizing the noise in the Noise phase to weights that disproportionally contribute to the harmful behavior should get a better retain-forget tradeoff. It doesn't need to be perfect, just better than random, so it should be doable with current methods.

There was a unit conversion mistake, it should have been 80 minutes. Now fixed.

Besides that, I agree with everything here; these will all be fixed in the final blog post. I already looked at one of the 30m-1h questions and it appeared to be doable in ~3 minutes with the ability to ctrl-f transcripts but would take longer without transcripts, unknown how long.

In the next version I will probably use the no-captions AI numbers and measure myself without captions to get a rough video speed multiplier, then possibly do better stats that separate out domains with strong human-time-dependent difficulty from domains without (like this and SWE-Lancer).

I would love to have Waymo data. It looks like it's only available since September 2024 so I'll still need to use Tesla for the earlier period. More critically they don't publish disengagement data, only crash/injury. There are Waymo claims of things like 1 disengagement every 17,000 miles but I don't believe them without a precise definition for what this number represents.

Cross-domain time horizon: 

We know AI time horizons (human time-to-complete at which a model has a 50% success rate) on software tasks are currently ~1.5hr and doubling every 4-7 months, but what about other domains? Here's a preliminary result comparing METR's task suite (orange line) to benchmarks in other domains, all of which have some kind of grounding in human data:

Observations

• Time horizons on agentic computer use (OSWorld) is ~100x shorter than other domains. Domains like Tesla self-driving (tesla_fsd), scientific knowledge (gpqa), and math contests (aime), video understanding (video_mme), and software (hcast_r_s) all have roughly similar horizons.
  • My guess is this means models are good at taking in information from a long context but bad at acting coherently. Most work requires agency like OSWorld, which may be why AIs can't do the average real-world 1-hour task yet.
  • There are likely other domains that fall outside this cluster; these are just the five I examined
  • Note the original version had a unit conversion error that gave 60x too high horizons for video_mme; this has been fixed (thanks @ryan_greenblatt )
• Rate of improvement varies significantly; math contests have improved ~50x in the last year but Tesla self-driving only 6x in 3 years.
• HCAST is middle of the pack in both.

Note this is preliminary and uses a new methodology so there might be data issues. I'm currently writing up a full post!

Is this graph believable? What do you want to see analyzed?

edit: fixed Video-MME numbers

The dates used in our regression are the dates models were publicly released, not the dates we benchmarked them. If we use the latter dates, or the dates they were announced, I agree they would be more arbitrary.

Also, there is lots of noise in a time horizon measurement and it only displays any sort of pattern because we measured over many orders of magnitude and years. It's not very meaningful to extrapolate from just 2 data points; there are many reasons one datapoint could randomly change by a couple of months or factor of 2 in time horizon. 

• Release schedules could be altered
• A model could be overfit to our dataset
• One model could play less well with our elicitation/scaffolding
• One company could be barely at the frontier, and release a slightly-better model right before the leading company releases a much-better model.

All of these factors are averaged out if you look at more than 2 models. So I prefer to see each model as evidence of whether the trend is accelerating or slowing down over the last 1-2 years, rather than an individual model being very meaningful.

Some versions of the METR time horizon paper from alternate universes:

Measuring AI Ability to Take Over Small Countries (idea by Caleb Parikh)

Abstract: Many are worried that AI will take over the world, but extrapolation from existing benchmarks suffers from a large distributional shift that makes it difficult to forecast the date of world takeover. We rectify this by constructing a suite of 193 realistic, diverse countries with territory sizes from 0.44 to 17 million km^2. Taking over most countries requires acting over a long time horizon, with the exception of France. Over the last 6 years, the land area that AI can successfully take over with 50% success rate has increased from 0 to 0 km^2, at the rate of 0 km^2 per year (95% CI 0.0-0.0 km^2/year); extrapolation suggests that AI world takeover is unlikely to occur in the near future. To address concerns about the narrowness of our distribution, we also study AI ability to take over small planets and asteroids, and find similar trends.

Measuring AI Ability to Worry About AI

Abstract: Since 2019, the amount of time LW has spent worrying about AI has doubled every seven months, and now constitutes the primary bottleneck to AI safety research. Automation of worrying would be transformative to the research landscape, but worrying includes several complex behaviors, ranging from simple fretting to concern, anxiety, perseveration, and existential dread, and so is difficult to measure. We benchmark the ability of frontier AIs to worry about common topics like disease, romantic rejection, and job security, and find that current frontier models such as Claude 3.7 Sonnet already outperform top humans, especially in existential dread. If these results generalize to worrying about AI risk, AI systems will be capable of autonomously worrying about their own capabilities by the end of this year, allowing us to outsource all our AI concerns to the systems themselves.

Estimating Time Since The Singularity

Early work on the time horizon paper used a hyperbolic fit, which predicted that AGI (AI with an infinite time horizon) was reached last Thursday. [1] We were skeptical at first because the R^2 was extremely low, but recent analysis by Epoch suggested that AI already outperformed humans at a 100-year time horizon by about 2016. We have no choice but to infer that the Singularity has already happened, and therefore the world around us is a simulation. We construct a Monte Carlo estimate over dates since the Singularity and simulator intentions, and find that the simulation will likely be turned off in the next three to six months.

[1]: This is true

I basically agree with this. The reason the paper didn't include this kind of reasoning (only a paragraph about how AGI will have infinite horizon length) is we felt that making a forecast based on a superexponential trend would be too much speculation for an academic paper. (There is really no way to make one without heavy reliance on priors; does it speed up by 10% per doubling or 20%?) It wasn't necessary given the 2027 and 2029-2030 dates for 1-month AI derived from extrapolation already roughly bracketed our uncertainty.