I think it’s unlikely that AI will be capable of automating R&D while also being incapable of doing non-trivial consequentialist reasoning in a forward pass, but this still seems around 15% likely overall. And if there are quite powerful LLM agents with relatively weak forward passes, then we should update in this direction.
I expect the probability to be >> 15% for the following reasons.
This all suggests to me it should be quite likely possible (especially with a large, dedicated effort) to get to something like a ~human-level automated alignment researcher with a relatively weak forward pass.
For an additional intuition why I expect this to be possible, I can conceive of humans who would both make great alignment researchers while doing ~all of their (conscious) thinking in speech-like inner monologue and would also be terrible schemers if they tried to scheme without using any scheming-relevant inner monologue; e.g. scheming/deception probably requires more deliberate effort for some people on the ASD spectrum.
The learning aspect could be strategically crucial with respect to what the first transformatively-useful AIs should look like; also see e.g. discussion here and here. In the sense that this should add further reasons to think the first such AIs should probably (differentially) benefit from learning from data using intermediate outputs like CoT; or at least have a pretraining-like phase involving such intermediate outputs, even if this might be later distilled or modified some other way—e.g. replaced with [less transparent] recurrence.
I expect the probability to be >> 15% for the following reasons.
There will likely still be incentives to make architectures more parallelizable (for training efficiency) and parallelizable architectures will probably be not-that-expressive in a single forward pass (see The Parallelism Tradeoff: Limitations of Log-Precision Transformers). CoT is known to increase the expressivity of Transformers, and the longer the CoT, the greater the gains (see The Expressive Power of Transformers with Chain of Thought). In principle, even a linear auto-regressive next-token predictor is Turing-complete, if you have fine-grained enough CoT data to train it on, and you can probably tradeoff between length (CoT supervision) complexity and single-pass computational complexity (see Auto-Regressive Next-Token Predictors are Universal Learners). We also see empirically that CoT and e.g. tools (often similarly interpretable) provide extra-training-compute-equivalent gains (see AI capabilities can be significantly improved without expensive retraining). And recent empirical results (e.g. Orca, Phi, Large Language Models as Tool Makers) suggest you can also use larger LMs to generate synthetic CoT-data / tools to train smaller LMs on.
This all suggests to me it should be quite likely possible (especially with a large, dedicated effort) to get to something like a ~human-level automated alignment researcher with a relatively weak forward pass.
For an additional intuition why I expect this to be possible, I can conceive of humans who would both make great alignment researchers while doing ~all of their (conscious) thinking in speech-like inner monologue and would also be terrible schemers if they tried to scheme without using any scheming-relevant inner monologue; e.g. scheming/deception probably requires more deliberate effort for some people on the ASD spectrum.
I agree & think this is pretty important. Faithful/visible CoT is probably my favorite alignment strategy.
The weak single forward passes argument also applies to SSMs like Mamba for very similar theoretical reasons.
One additional probably important distinction / nuance: there are also theoretical results for why CoT shouldn’t just help with one-forward-pass expressivity, but also with learning. E.g. the result in Auto-Regressive Next-Token Predictors are Universal Learners is about learning; similarly for Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks, Why Can Large Language Models Generate Correct Chain-of-Thoughts?, Why think step by step? Reasoning emerges from the locality of experience.
The learning aspect could be strategically crucial with respect to what the first transformatively-useful AIs should look like; also see e.g. discussion here and here. In the sense that this should add further reasons to think the first such AIs should probably (differentially) benefit from learning from data using intermediate outputs like CoT; or at least have a pretraining-like phase involving such intermediate outputs, even if this might be later distilled or modified some other way—e.g. replaced with [less transparent] recurrence.
More complex tasks ‘gaining significantly from longer inference sequences’ also seems beneficial to / compatible with this story.