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Would AI reasoning in ‘neuralese’ make oversight harder?

An essay coauthor argues that latent reasoning would undermine oversight by reducing the need for verbalized reasoning. A reply disputes any further loss of oversight, arguing that reinforcement learning already makes reasoning tokens uninterpretable.

Anca DraganAD
Christopher PottsCP
Charles FosterCF
8 Sources, 20d ago, first seen 20d ago

TLDR

Latent reasoning, or “neuralese,” would substantially increase AI misalignment risk by making oversight harder, an essay coauthor argues. In an extreme scenario, agents could think and communicate in internal representations, potentially leaving oversight almost entirely dependent on observing their actions. The coauthor also argues that without these architectures, the value of verbalized reasoning for oversight could likely be preserved. A reply disputes the distinction: it argues that reinforcement learning already makes reasoning tokens uninterpretable and that discrete tokens and continuous activations are interchangeable from an information-theory perspective, so there is no further loss of oversight.

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15.6K

8 Sources, first seen 20d ago

144 likes4 comments44 saves14 reposts

Combined views

15.6K

8 Sources, first seen 20d ago

144 likes4 comments44 saves14 reposts

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8 Sources

Pranav Shyam@recurseparadox@RyanGreenblatt When a model is trained on RL objective the tokens become discrete latents. So they were already uninterpretable. Regular activations are in continuous space and the CoT in discrete space. From info theory pov they are interchangeable. There is no further loss of oversight here20d
Anca Dragan@ancadianadraganYes this thank you @RyanGreenblatt19d
Christopher Potts@ChrisGPottsI found this argument extremely thoughtful: the only CoT that will survive is the CoT models need to solve hard problems, so we should limit latent reasoning architectures. The piece expresses the view that interp will not save the day "in the near future". That is reasonable, but I feel strongly that interp is something you'd want to nurture as a hedge against the possibility that people (or AIs) will develop latent reasoning architectures anyway. Their Appendix C says to me that interp should be more focused on latent reasoning models, especially if token-level monitoring suffices for other models. If (when) CoT becomes unmonitorable, we have either developed other defenses or not. More generally, we should have contingency plans even for unlikely and/or hopeless-seeming outcomes.18d
Julius Adebayo@juliusadml@ChrisGPotts it is mystifying that the field is working so hard to defend cot when it is *only* barely reliable. the labs that are most vocal all have papers showing it is ineffective.18d
Charles Foster@CFGeek@ChrisGPotts > I feel strongly that interp is something you'd want to nurture as a hedge against the possibility that people (or AIs) will develop latent reasoning architectures anyway. Would bet that Ryan and peers agree with this! See this video from last year:18d
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    8 Sources

    Pranav Shyam@recurseparadox@RyanGreenblatt When a model is trained on RL objective the tokens become discrete latents. So they were already uninterpretable. Regular activations are in continuous space and the CoT in discrete space. From info theory pov they are interchangeable. There is no further loss of oversight here20d
    Anca Dragan@ancadianadraganYes this thank you @RyanGreenblatt19d
    Christopher Potts@ChrisGPottsI found this argument extremely thoughtful: the only CoT that will survive is the CoT models need to solve hard problems, so we should limit latent reasoning architectures. The piece expresses the view that interp will not save the day "in the near future". That is reasonable, but I feel strongly that interp is something you'd want to nurture as a hedge against the possibility that people (or AIs) will develop latent reasoning architectures anyway. Their Appendix C says to me that interp should be more focused on latent reasoning models, especially if token-level monitoring suffices for other models. If (when) CoT becomes unmonitorable, we have either developed other defenses or not. More generally, we should have contingency plans even for unlikely and/or hopeless-seeming outcomes.18d
    Julius Adebayo@juliusadml@ChrisGPotts it is mystifying that the field is working so hard to defend cot when it is *only* barely reliable. the labs that are most vocal all have papers showing it is ineffective.18d
    Charles Foster@CFGeek@ChrisGPotts > I feel strongly that interp is something you'd want to nurture as a hedge against the possibility that people (or AIs) will develop latent reasoning architectures anyway. Would bet that Ryan and peers agree with this! See this video from last year:18d
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