I think you’re overstating the impact of interpretability here. Your earlier point that latent reasoning models can’t be trained very well and that discretization may be load bearing rather than a readability tax in addition to significant inference infra hurdles (e.g. batching, speculative decoding) have limited any serious attempts and reduced the theoretical advantage over CoT at least in the near term.
reply> I think you’re overstating the impact of interpretability here
replyOutside of RLAIF, interpretability is the strongest way to do alignment right now. alignment is important because otherwise LLMs are incentivized to learn power seeking, dangerous behaviours [1]. a more downto earth example of alignment being important is that agents are incentivized to do tasks in the shortest way possible, and this way might not be what the user wants (I explain this further in another comment in this thread)
[1] https://www.forbes.com/sites/boazsobrado/2026/03/11/alibabas...
You’re putting the cart before the horse - alignment is an unsolved challenge (there are proposed approaches and active research on this) but it is still not established (beyond theory) that latent reasoning is more capable than CoT on hard language reasoning, particularly at scale.
replyMost alignment methods nowadays don't rely on interpretability. And neither do all LLM vendors care about alignment much - especially not in China.
replyThose things being untrainable at scale is why they aren't around. Alignment is an afterthought.
China should care: https://www.forbes.com/sites/boazsobrado/2026/03/11/alibabas...
replyAs is, Chinese labs spend more effort on "rhetorical alignment to the party line" than alignment of any other kind.
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