Partial Observability and Rationality in Human-AI Interaction

Reinforcement Learning from Human Feedback, or RLHF, is one of the main ways that makers of large language models make them 'aligned'. But people have long noted that there are difficulties with this approach when the models are smarter than the humans providing feedback. In this episode, I talk with Scott Emmons about his work categorizing the problems that can show up in this setting.

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The transcript: https://axrp.net/episode/2024/06/12/episode-33-rlhf-problems-scott-emmons.html

Topics we discuss, and timestamps:

0:00:33 - Deceptive inflation

0:17:56 - Overjustification

0:32:48 - Bounded human rationality

0:50:46 - Avoiding these problems

1:14:13 - Dimensional analysis

1:23:32 - RLHF problems, in theory and practice

1:31:29 - Scott's research program

1:39:42 - Following Scott's research

Scott's website: https://www.scottemmons.com

Scott's X/twitter account: https://x.com/emmons_scott

When Your AIs Deceive You: Challenges With Partial Observability of Human Evaluators in Reward Learning: https://arxiv.org/abs/2402.17747

Other works we discuss:

AI Deception: A Survey of Examples, Risks, and Potential Solutions: https://arxiv.org/abs/2308.14752

Uncertain decisions facilitate better preference learning: https://arxiv.org/abs/2106.10394

Invariance in Policy Optimisation and Partial Identifiability in Reward Learning: https://arxiv.org/abs/2203.07475

The Humble Gaussian Distribution (aka principal component analysis and dimensional analysis): http://www.inference.org.uk/mackay/humble.pdf

Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!: https://arxiv.org/abs/2310.03693

Episode art by Hamish Doodles: hamishdoodles.com