If you succesfully build a highly capable “aligned” model (according to some class of definitions that Anthropic would use for the words “capable” and “aligned”) and it brings about a global dark age of poverty and inequality by completely eliminating the value of labor vs capital, can you still call it aligned?
If the answer is “yes”, our definition of alignment kind of sucks.
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zozbot234
Note that this result actually turns out to generalize well beyond Claude itself: Anthropic has actually conducted very similar research on open weight models, which they call Model Spec Midtraining https://arxiv.org/abs/2605.02087 (discussed at https://alignment.anthropic.com/2026/msm ) and they have released fine tuned versions of open models trained for a variety of toy "values" (Llama 3.1 8B, Qwen 2.5 32B, Qwen 3 32B) in order to show how the elicitation of these values in any one training context shapes the model's response to tangentially related questions: https://github.com/chloeli-15/model_spec_midtraininghttps://huggingface.co/chloeli/collections Very exciting to see this continued interaction with the open weights community, after the earlier NLA paper!
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einrealist
Isn't alignment a dilemma?
Because what is aligned, how and for whom? And who decides how that alignment should look like? There are probably many domains in which required alignment is in conflict with each other (e.g. using LLMs for warfare vs. ethically based domains). I can't imagine how this can be viable on the required scale (like one model per domain) for the already huge investments.
soletta
This reinforces my suspicion that alignment and training in general is closer to being a pedagogical problem than anything else. Given a finite amount of training input, how do we elicit the desired model behavior? I’m not sure if asking educators is the right answer, but it’s one place to start.
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roenxi
One of the lessons of philosophy is that once you adopt any particular value system, almost all philosophers either become immoral or caught up in meaningless and trivial quibbles. This sort of alignment work is quite interesting because it looks like we might be about to re-tread the history of philosophy at a speedrun pace in the AI world. It'll be interesting to watch.
For anyone who isn't keeping up there is also work being done [0] to understand how models model ethical considerations internally. Mainly, one suspects, to make the open models less ethical on demand rather than to support alignment. Turns out that models tend to learn some sort of "how moral is this?" axis internally when refusing queries that can be identified and interfered with.
Assuming rules and principles are something like first- and second- derivatives of optimized equations for a given domain, it makes sense to teach/train them in the context of derivation and integration. It would be fascinating to use existing case-based literature from e.g., business, law, or medicine for the training.
A related question for setting intent for integration/testing: instead of stating the goal, pedagogy in those fields state the concrete problem and ask the student for an answer before they've been taught the principles or approaches, as a way of motivating the training (a bit like philosophers posing paradoxes). I'd be very curious whether LLM's are sensitive to this kind of direction, and if it produces better results. The theory for case-based discipline is that you don't want people to just apply rules; it's the flip side of working from first principles, to engage all the relevant and concerning facts instead of omitting those that don't fit the rule. I suspect LLM's could actually be good at this.
MeteorMarc
Count the lessons below "We’ve learned four main lessons from this work:" and laugh.
bicx
Side note: Anthropic has done well at achieving an immediately-recognizable art style.
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datadrivenangel
Why do they have cancer research listed on these charts as a misalignment issue?
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siva7
Teaching Claude to maximize shareholder value. Make no mistake to assume ai alignment has any different meaning for anthropic leadership.
unchocked
This lowers p(doom) for me.
It makes sense that reinforcement learning on reasoning about coherent principles should bias toward principled action in real situations.
Probably also illuminates moral interpretability.
shevy-java
Now the foolish humans are training Claude Skynet to become smarter.
If you succesfully build a highly capable “aligned” model (according to some class of definitions that Anthropic would use for the words “capable” and “aligned”) and it brings about a global dark age of poverty and inequality by completely eliminating the value of labor vs capital, can you still call it aligned?
If the answer is “yes”, our definition of alignment kind of sucks.
Note that this result actually turns out to generalize well beyond Claude itself: Anthropic has actually conducted very similar research on open weight models, which they call Model Spec Midtraining https://arxiv.org/abs/2605.02087 (discussed at https://alignment.anthropic.com/2026/msm ) and they have released fine tuned versions of open models trained for a variety of toy "values" (Llama 3.1 8B, Qwen 2.5 32B, Qwen 3 32B) in order to show how the elicitation of these values in any one training context shapes the model's response to tangentially related questions: https://github.com/chloeli-15/model_spec_midtraining https://huggingface.co/chloeli/collections Very exciting to see this continued interaction with the open weights community, after the earlier NLA paper!
Isn't alignment a dilemma?
Because what is aligned, how and for whom? And who decides how that alignment should look like? There are probably many domains in which required alignment is in conflict with each other (e.g. using LLMs for warfare vs. ethically based domains). I can't imagine how this can be viable on the required scale (like one model per domain) for the already huge investments.
This reinforces my suspicion that alignment and training in general is closer to being a pedagogical problem than anything else. Given a finite amount of training input, how do we elicit the desired model behavior? I’m not sure if asking educators is the right answer, but it’s one place to start.
One of the lessons of philosophy is that once you adopt any particular value system, almost all philosophers either become immoral or caught up in meaningless and trivial quibbles. This sort of alignment work is quite interesting because it looks like we might be about to re-tread the history of philosophy at a speedrun pace in the AI world. It'll be interesting to watch.
For anyone who isn't keeping up there is also work being done [0] to understand how models model ethical considerations internally. Mainly, one suspects, to make the open models less ethical on demand rather than to support alignment. Turns out that models tend to learn some sort of "how moral is this?" axis internally when refusing queries that can be identified and interfered with.
[0] https://github.com/p-e-w/heretic
Assuming rules and principles are something like first- and second- derivatives of optimized equations for a given domain, it makes sense to teach/train them in the context of derivation and integration. It would be fascinating to use existing case-based literature from e.g., business, law, or medicine for the training.
A related question for setting intent for integration/testing: instead of stating the goal, pedagogy in those fields state the concrete problem and ask the student for an answer before they've been taught the principles or approaches, as a way of motivating the training (a bit like philosophers posing paradoxes). I'd be very curious whether LLM's are sensitive to this kind of direction, and if it produces better results. The theory for case-based discipline is that you don't want people to just apply rules; it's the flip side of working from first principles, to engage all the relevant and concerning facts instead of omitting those that don't fit the rule. I suspect LLM's could actually be good at this.
Count the lessons below "We’ve learned four main lessons from this work:" and laugh.
Side note: Anthropic has done well at achieving an immediately-recognizable art style.
Why do they have cancer research listed on these charts as a misalignment issue?
Teaching Claude to maximize shareholder value. Make no mistake to assume ai alignment has any different meaning for anthropic leadership.
This lowers p(doom) for me.
It makes sense that reinforcement learning on reasoning about coherent principles should bias toward principled action in real situations.
Probably also illuminates moral interpretability.
Now the foolish humans are training Claude Skynet to become smarter.
When will they ever learn ...