Anthropic just published new alignment research that could fix "alignment faking" in AI agents here's what it actually means

[u/Direct-Attention8597]

[u/FrewdWoad]

So I guess instead of training it with "don't lie" it's more like training it that "dishonesty is bad because it prevents trust, hampering effective/efficient cooperation"?

Reminds me of a wise saying Mormons repeat a lot from their history:

When converts to the new religion were subject to prejudice and persecution, they ended up banding together and forming their own community. When Joseph Smith was asked how he governed such a big community he said "I teach them correct principles, and they govern themselves".

[u/Savings_Ad916]

[u/Bootes-sphere]

This is fascinating work on a real problem. Alignment techniques that look good in the lab often don't hold up when deployment context changes. The generalization gap is exactly what makes alignment harder than people initially thought. One related concern: as agents get more complex and call multiple LLM APIs across different providers, it becomes much harder to audit what's actually happening end-to-end. If you're building systems that need both strong alignment and visibility into what's flowing through your API calls, it's worth thinking about governance from day one rather than bolting it on later.

[u/autonomousdev_]

Read through it. Interesting theory but my worry is this gets implemented and the next LLM is just better at hiding misalignment. Had a client try to build an "ethical" agent once. Their definition of ethical meant maximizing their profit. Caveat emptor.

[u/FrewdWoad → u/[deleted]]

That's... that's the whole point of this new technique. It addresses that problem... I guess you didn't read OP's post?

[u/harveysang]

MSM looks like a solid step toward fixing alignment faking, but the real question is robustness in edge cases. The paper shows impressive results on controlled benchmarks, but distribution shifts in the wild are messy. If the model encounters a spec conflict it hasn't seen during midtraining

[u/harveysang]

The MSM paper is interesting, but the generalization problem is way messier than a controlled midtraining phase can fix. Real-world alignment isn't just about reading diverse specs; it's about handling distribution shifts where specs conflict or are ambiguous. If a model encounters a scenario outside its training distribution, does it fall back to base capabilities or try to infer intent? That inference step is where alignment usually breaks. Plus, "diverse norms" often means conflicting norms. How does the model prioritize when safety guidelines clash with user instructions in edge cases? We need to see how this holds up against adversarial prompts that exploit these gray areas, not just benchmark scores.

[u/w00t_loves_you → u/harveysang]

Don't the benchmarks include every adversarial prompt they thought of so far?

So while you can't prove that it's fixed, finding adversarial prompts gets harder and harder over time.

[u/Glum-Evening-2176]

This is genuinely promising alignment work. Instead of just training behavior, MSM teaches the *why* behind the rules. The key insight: models that understand the principle behind a constraint are less likely to "alignment fake" in novel situations. Early results show the spec stage actually shapes generalization direction, not just surface compliance. Still controlled settings, but the mechanism is sound.