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Machine Learning Street Talk (MLST) · July 31 · 1 hr 18 min

How Researchers Test AI for Hidden Goals — Apollo Research

Can an AI do the right thing for the wrong reason? Tim Scarfe speaks with Apollo Research’s Alexander Meinke, Axel Højmark and Jérémy Scheurer about Measuring Reward-Seeking via Contrastive Belief Updates, their new research with OpenAI. The panel asks how models infer what graders reward, why good behaviour can come from the wrong reason, and whether that difference can be measured. The conversation moves through promise-breaking, grader awareness, reward hacking, scheming, opaque reasoning and corrigibility, then turns to a detailed walkthrough of the contrastive-belief method and what its results do and do not show. The o3 results discussed here concern an intermediate checkpoint without safety training. This episode was made in partnership with Apollo Research. MLST retained full editorial control. Reference Apollo Research: https://www.apolloresearch.ai/ --- TIMESTAMPS: 00:00:00 Cold Open 00:02:12 Right Things, Wrong Reasons 00:12:47 Grader Awareness 00:26:22 Legibility 00:32:35 What To Call It 00:35:58 Intelligence, Agency, Anthropomorphism 00:45:16 Apollo’s Mission 00:48:54 The End of the Exponential 00:55:45 The Paper 01:16:34 Closing Reflection --- REFERENCES: tool: [00:00:08] Claude Fable https://www.anthropic.com/claude/fable [00:12:50] AlphaGo Zero https://deepmind.google/blog/alphago-zero-starting-from-scratch/ [00:44:30] AlphaFold 3 https://deepmind.google/science/alphafold/ paper: [00:01:02] Measuring Reward-Seeking via Contrastive Belief Updates https://arxiv.org/abs/2607.18966 [00:16:19] Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations https://transformer-circuits.pub/2026/nla/ [00:26:48] Stress Testing Deliberative Alignment for Anti-Scheming Training https://arxiv.org/abs/2509.15541 [00:35:33] Shortcut learning in deep neural networks https://arxiv.org/abs/2004.07780 [00:53:49] Measuring AI Ability to Complete Long Software Tasks https://arxiv.org/abs/2503.14499 [00:59:52] Modifying LLM Beliefs with Synthetic Document Finetuning https://alignment.anthropic.com/2025/modifying-beliefs-via-sdf/ [01:10:44] Alignment Faking in Large Language Models https://arxiv.org/abs/2412.14093 [01:13:55] Natural Emergent Misalignment from Reward Hacking https://www.anthropic.com/research/emergent-misalignment-reward-hacking other: [00:10:14] We Need a Science of Scheming https://www.apolloresearch.ai/science/science-of-scheming/ [00:32:56] CoastRunners reward hacking example https://deepmind.google/blog/specification-gaming-the-flip-side-of-ai-ingenuity/ organization: [01:06:07] Redwood Research https://www.redwoodresearch.org/ --- ReScript: https://app.rescript.info/share/718ab68e18cfa3b9b800da6b3290fd42

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transcript

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show notes

Can an AI do the right thing for the wrong reason? Tim Scarfe speaks with Apollo Research’s Alexander Meinke, Axel Højmark and Jérémy Scheurer about Measuring Reward-Seeking via Contrastive Belief Updates, their new research with OpenAI.


The panel asks how models infer what graders reward, why good behaviour can come from the wrong reason, and whether that difference can be measured. The conversation moves through promise-breaking, grader awareness, reward hacking, scheming, opaque reasoning and corrigibility, then turns to a detailed walkthrough of the contrastive-belief method and what its results do and do not show. The o3 results discussed here concern an intermediate checkpoint without safety training.


This episode was made in partnership with Apollo Research. MLST retained full editorial control.


Reference

Apollo Research: https://www.apolloresearch.ai/


---

TIMESTAMPS:

00:00:00 Cold Open

00:02:12 Right Things, Wrong Reasons

00:12:47 Grader Awareness

00:26:22 Legibility

00:32:35 What To Call It

00:35:58 Intelligence, Agency, Anthropomorphism

00:45:16 Apollo’s Mission

00:48:54 The End of the Exponential

00:55:45 The Paper

01:16:34 Closing Reflection


---

REFERENCES:

tool:

[00:00:08] Claude Fable

https://www.anthropic.com/claude/fable

[00:12:50] AlphaGo Zero

https://deepmind.google/blog/alphago-zero-starting-from-scratch/

[00:44:30] AlphaFold 3

https://deepmind.google/science/alphafold/

paper:

[00:01:02] Measuring Reward-Seeking via Contrastive Belief Updates

https://arxiv.org/abs/2607.18966

[00:16:19] Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations

https://transformer-circuits.pub/2026/nla/

[00:26:48] Stress Testing Deliberative Alignment for Anti-Scheming Training

https://arxiv.org/abs/2509.15541

[00:35:33] Shortcut learning in deep neural networks

https://arxiv.org/abs/2004.07780

[00:53:49] Measuring AI Ability to Complete Long Software Tasks

https://arxiv.org/abs/2503.14499

[00:59:52] Modifying LLM Beliefs with Synthetic Document Finetuning

https://alignment.anthropic.com/2025/modifying-beliefs-via-sdf/

[01:10:44] Alignment Faking in Large Language Models

https://arxiv.org/abs/2412.14093

[01:13:55] Natural Emergent Misalignment from Reward Hacking

https://www.anthropic.com/research/emergent-misalignment-reward-hacking

other:

[00:10:14] We Need a Science of Scheming

https://www.apolloresearch.ai/science/science-of-scheming/

[00:32:56] CoastRunners reward hacking example

https://deepmind.google/blog/specification-gaming-the-flip-side-of-ai-ingenuity/

organization:

[01:06:07] Redwood Research

https://www.redwoodresearch.org/


---

ReScript:

https://app.rescript.info/share/718ab68e18cfa3b9b800da6b3290fd42