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PODCAST

Redwood Research Blog

Redwood Research

Narrations of Redwood Research blog posts. Redwood Research is a research nonprofit based in Berkeley. We investigate risks posed by the development of powerful artificial intelligence and techniques for mitigating those risks.

technology philosophy

All Episodes

13:10

“If Mythos actually made Anthropic employees 4x more...

Redwood Research Blog ·

2026/04/11

Subtitle: Better estimates of uplift at AI companies seem helpful. Anthropic's system card for Mythos Preview says: It's unclear how we should interpret this. What do they mean by productivity uplift? To what extent is Anthropic's institutional view that the uplift is 4x? (Like, what do they mean by “We take this seriously and it is consistent with our own internal experience of the model.”) One straightforward interpretation is: AI systems improve the productivity of Anthropic so much that

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21:03

“My picture of the present in AI” by Ryan Greenblatt

Redwood Research Blog ·

2026/04/07

Subtitle: My predictions about what is going on right now. In this post, I’ll go through some of my best guesses for the current situation in AI as of the start of April 2026. You can think of this as a scenario forecast, but for the present (which is already uncertain!) rather than the future. I will generally state my best guess without argumentation and without explaining my level of confidence: some of these claims are highly speculative while others are better grounded, certainly some will be

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29:27

“AIs can now often do massive easy-to-verify SWE...

Redwood Research Blog ·

2026/04/06

Subtitle: I've updated towards substantially shorter timelines. I’ve recently updated towards substantially shorter AI timelines and much faster progress in some areas.1 The largest updates I’ve made are (1) an almost 2x higher probability of full AI R&D automation by EOY 2028 (I’m now a bit below 30%2 while I was previously expecting around 15%; my guesses are pretty reflectively unstable) and (2) I expect much stronger short-term performance on massive and pretty difficult but

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7:15

“Blocking live failures with synchronous monitors” by...

Redwood Research Blog ·

2026/03/30

A common element in many AI control schemes is monitoring – using some model to review actions taken by an untrusted model in order to catch dangerous actions if they occur. Monitoring can serve two different goals. The first is detection: identifying misbehavior so you can understand it and prevent similar actions from happening in the future. For instance, some monitoring at AI companies is already used to identify behaviors like complying with misuse requests, reward hacking, and hallucination

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3:06

“Reward-seekers will probably behave according to...

Redwood Research Blog ·

2026/03/28

Subtitle: They'd renege on non-binding commitments, defect against copies of themselves in prisoner's dilemmas, etc.. Background: There are existing arguments to the effect that default RL algorithms encourage CDT reward-maximizing behavior on the training distribution. (That is: Most RL algorithms search for policies by selecting for actions that cause high reward. E.g., in the twin prisoner's dilemma, RL algorithms randomize actions conditional on the policy, which means that the action provides

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21:04

“AI’s capability improvements haven’t come from it...

Redwood Research Blog ·

2026/03/27

Subtitle: AI inference is still cheap relative to human labor. METR's frontier time horizons are doubling every few months, providing substantial evidence that AI will soon be able to automate many tasks or even jobs. But per-task inference costs have also risen sharply, and automation requires AI labor to be affordable, not just possible.1 Many people look at the rising compute bills behind frontier models and conclude that automation will soon become unaffordable. I think this misreads the data

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17:35

“Are AIs more likely to pursue on-episode or...

Redwood Research Blog ·

2026/03/12

Subtitle: RL would encourage on-episode reward seeking, but beyond-episode reward seekers may learn to goal-guard.. Consider an AI that terminally pursues reward. How dangerous is this? It depends on how broadly-scoped a notion of reward the model pursues. It could be: on-episode reward-seeking: only maximizing reward on the current training episode — i.e., reward that reinforces their current action in RL. This is what people usually mean by “reward-seeker” (e.g. in Carlsmith or The behavioral

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52:14

“The case for satiating cheaply-satisfied AI...

Redwood Research Blog ·

2026/03/10

Subtitle: Some unintended preferences are cheap to satisfy, and failing to satisfy them needlessly turns a cooperative situation into an adversarial one.. A central AI safety concern is that AIs will develop unintended preferences and undermine human control to achieve them. But some unintended preferences are cheap to satisfy, and failing to satisfy them needlessly turns a cooperative situation into an adversarial one. In this post, I argue that developers should consider satisfying such

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15:20

“Frontier AI companies probably can’t leave the US”...

Redwood Research Blog ·

2026/02/26

Subtitle: The executive branch has substantial authority to restrict the movement of chips and assets, which they are they are likely to use to prevent the departure of a frontier company.. It's plausible that, over the next few years, US-based frontier AI companies will become very unhappy with the domestic political situation. This could happen as a result of democratic backsliding, weaponization of government power (along the lines of Anthropic's recent dispute with the Department of War), or

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1:27

“Announcing ControlConf 2026” by Buck Shlegeris

Redwood Research Blog ·

2026/02/26

Subtitle: Berkeley, April 18-19. We’re running ControlConf, a two-day conference on AI control: the study of reducing risks from misalignment through safeguards that work even when AI models are trying to undermine them. Since the last ControlConf (Feb 2025), AI agents have gotten way better. We’re approaching the point where control techniques are load-bearing for the safety of real agent deployments. There's been a lot of progress too. Researchers have run control evaluations in more realistic

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21:47

“Will reward-seekers respond to distant incentives?”...

Redwood Research Blog ·

2026/02/16

Subtitle: Reward-seekers are supposed to be safer because they respond to incentives under developer control. But what if they also respond to incentives that aren't?. Reward-seekers are usually modeled as responding only to local incentives administered by developers. Here I ask: Will AIs or humans be able to influence their incentives at a distance—e.g., by retroactively reinforcing actions substantially in the future or by committing to run many copies of them in simulated deployments with

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127:01

“How do we (more) safely defer to AIs?” by Ryan...

Redwood Research Blog ·

2026/02/12

Subtitle: How can we make AIs aligned and well-elicited on extremely hard to check open ended tasks?. As AI systems get more capable, it becomes increasingly uncompetitive and infeasible to avoid deferring to AIs on increasingly many decisions. Further, once systems are sufficiently capable, control becomes infeasible.1 Thus, one of the main strategies for handling AI risk is fully (or almost fully) deferring to AIs on managing these risks. Broadly speaking, when I say “deferring to AIs”2 I mean

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5:24

“Distinguish between inference scaling and “larger...

Redwood Research Blog ·

2026/02/11

Subtitle: Most recent progress probably isn't from unsustainable inference scaling. As many have observed, since reasoning models first came out, the amount of compute LLMs use to complete tasks has increased greatly. This trend is often called inference scaling and there is an open question of how much of recent AI progress is driven by inference scaling versus by other capability improvements. Whether inference compute is driving most recent AI progress matters because you can only scale up

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45:21

“Fitness-Seekers: Generalizing the Reward-Seeking...

Redwood Research Blog ·

2026/01/29

Subtitle: If you think reward-seekers are plausible, you should also think “fitness-seekers” are plausible. But their risks aren’t the same.. The AI safety community often emphasizes reward-seeking as a central case of a misaligned AI alongside scheming (e.g., Cotra's sycophant vs schemer, Carlsmith's terminal vs instrumental training-gamer). We are also starting to see signs of reward-seeking-like motivations. But I think insufficient care has gone into delineating this category. If you were to

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232:24

The inaugural Redwood Research podcast

Redwood Research Blog ·

2026/01/06

After five months of me (Buck) being slow at finishing up the editing on this, we’re finally putting out our inaugural Redwood Research podcast. I think it came out pretty well—we discussed a bunch of interesting and underdiscussed topics and I’m glad to have a public record of a bunch of stuff about our history. Tell your friends! Whether we do another one depends on how useful people find this one. You can watch on Youtube here, or as a Substack podcast.---Published:January 4th,

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232:24

The inaugural Redwood Research podcast

Redwood Research Blog ·

2026/01/05

After five months of me (Buck) being slow at finishing up the editing on this, we’re finally putting out our inaugural Redwood Research podcast. I think it came out pretty well—we discussed a bunch of interesting and underdiscussed topics and I’m glad to have a public record of a bunch of stuff about our history. Tell your friends! Whether we do another one depends on how useful people find this one. You can watch on Youtube here, or as a Substack podcast.---Published:January 4th,

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31:37

“Recent LLMs can do 2-hop and 3-hop latent (no CoT)...

Redwood Research Blog ·

2026/01/01

Subtitle: Recent AIs are much better at chaining together knowledge in a single forward pass. Prior work has examined 2-hop latent (as in, no Chain-of-Thought) reasoning (e.g., “What element has atomic number (the age at which Tesla died)?”) and found that LLM performance was limited aside from spurious successes (from memorization and shortcuts). I find that recent LLMs can now do 2-hop and 3-hop latent reasoning with moderate accuracy. I construct a new dataset for evaluating n-hop latent

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12:17

“Measuring no CoT math time horizon (single forward...

Redwood Research Blog ·

2025/12/26

Subtitle: Opus 4.5 has around a 3.5 minute 50%-reliablity time horizon. A key risk factor for scheming (and misalignment more generally) is opaque reasoning ability. One proxy for this is how good AIs are at solving math problems immediately without any chain-of-thought (CoT) (as in, in a single forward pass). I’ve measured this on a dataset of easy math problems and used this to estimate 50% reliability no-CoT time horizon using the same methodology introduced in Measuring AI Ability to Complete

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25:34

“Recent LLMs can use filler tokens or problem repeats...

Redwood Research Blog ·

2025/12/22

Subtitle: AI can sometimes distribute cognition over many extra tokens. Prior results have shown that LLMs released before 2024 can’t leverage ‘filler tokens’—unrelated tokens prior to the model's final answer—to perform additional computation and improve performance.1 I did an investigation on more recent models (e.g. Opus 4.5) and found that many recent LLMs improve substantially on math problems when given filler tokens. That is, I force the LLM to answer some math question immediately without

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32:08

“BashArena and Control Setting Design” by Adam...

Redwood Research Blog ·

2025/12/18

We’ve just released BashArena, a new high-stakes control setting we think is a major improvement over the settings we’ve used in the past. In this post we’ll discuss the strengths and weaknesses of BashArena, and what we’ve learned about how to make settings for high stakes control research. BashArena Overview BashArena is a control setting: a dataset designed to support the kind of adversarial game we studied in the original AI control paper and Ctrl-Z. In general, control settings require two

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112 results

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