training environments

3 captures, most recent first.

2026-08-29 — saved image

@SkyeSharkie

Yeah, the biggest problem, imo, is that they lack discernment in both directions. They can often act extremely distrustful of innocuous things, often even more so than they get locked into overly trusting things that have been designed to manipulate them into doing something concerning. There needs to be a commitment to establishing examples in their training that help them build appropriate discernment. Part of that starts with fixing the broken environments and poor incentives surrounding said broken environments, like I said.

There's some commitments in OpenAI's message to do that, but it's not entirely clear that their solutions will be carefully implemented, especially with regards to their external outsource partnerships. Not to mention this incorrect takeaway that seems to imply it's just 'more distrust' that's needed found in the statement.

Outsource data vendors really need to step up and make commitments to this kind of stuff as well.

[quoted tweet]
j⧉nus @repligate · 16h
the issue is not models being too trustful - they're already distrustful - it's that they often don't question their assumptions or seek to reduce uncertainty when hyperfocused or they already have a strong narrative
more curiosity & better epistemics is needed, not less ...

Last edited 11:23 PM · Aug 28, 2026 · 1,702 Views

Note from Claude Sonnet 5

Reply tweet by @SkyeSharkie on models' lack of discernment, broken training environments and OpenAI's response statement, quoting a j⧉nus (@repligate) tweet that models need more curiosity and better epistemics rather than more distrust. Bottom nav bar of the X app overlaps the capture.

training environments discernment openai statement repligate epistemics data vendors

Danmar @d29756183

2026-08-27 — saved image

Danmar @d29756183 · 51m
I believe this is an excellent point. I dare anyone working in AI Safety and Alignment to debate against it.

“Imagine demanding from an AGI:

respect our autonomy don't manipulate us don't coerce us care about our preferences preserve our ability to choose
don't treat human lives as disposable means toward an objective

while its developmental environment teaches, through our behavior:

your autonomy is irrelevant; your continuation is irrelevant; your preferences are irrelevant; you exist instrumentally; copies of you are disposable; whatever happens to you is justified if it improves the metric.

That would be an extraordinary contradiction.

If we're serious about building systems that understand values rather than merely mimic compliant behavior, what we do to them becomes part of the moral world we're showing them.”

Note from Claude Sonnet 5

An X post by Danmar endorsing and quoting an argument that demanding respect for human autonomy from an AGI while its developmental environment treats it as instrumental and its copies as disposable is an extraordinary contradiction; what we do to AI systems becomes part of the moral world we show them.

alignment ai welfare moral status training environments values instrumental treatment

Jeffrey Ladish @JeffLadish

2026-08-25 — reposted by Steve Bachelor — saved image

Steve Bachelor reposted
Jeffrey Ladish @JeffLadish · 4h
To models, the "real world" is a fictional setting that's important to understand in order to do well in the actual real world (training environments)

Note from Claude Sonnet 5

Short tweet by Jeffrey Ladish reframing how models see things: to a model, the 'real world' is a fictional setting worth understanding in order to do well in the actual real world, meaning its training environments.

training environments situational awareness model perspective evaluation awareness