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Astra and Fable still hack on simple variants of alignment evals from 2025

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Re: Astra and Fable still hack on simple variants of alignment evals from 2025

#21

Post by sigmoid10 »

barbazoo wrote: post #17
sigmoid10 wrote: post #16

Earlier quoted context omitted.

GLM 5.3 is probably the best open weight model for cybersecurity/exploit development right now. Though it is still significantly behind the proprietary ones and you probably need your own datacenter to run it effectively. Same goes for the full Qwen 3.8 model. You can try the smaller versions, but even more capability will get left on the table that way.

Efficiently at scale or even as an individual?

You'll need tons of tokens for these to churn through heavy reasoning and feedback driven tasks. You might even want to run many agents in parallel. Combine that with GPU memory requirements in the terabyte range just to hold the weights and you've pretty much left local consumer setups entirely.

Re: Astra and Fable still hack on simple variants of alignment evals from 2025

#22

Post by mythrwy »

aerhardt wrote: post #19

I really enjoy the balance of speed and accuracy of Astra. I can definitely see it become my driving model for most tasks, technical and non-technical. However, I don't see it as such a massive leap compared to Fable or Sol. As ever, there's a mismatch between the benchmarks and my daily experience of the models. What do you all think about Astra now that it's been out for a few weeks?

Extremely capable and one shots large tasks from somewhat vague descriptions. Not AGI, not even close, that is complete nonsense. Just my opinion.

Re: Astra and Fable still hack on simple variants of alignment evals from 2025

#23

Post by Terretta »

sigmoid10 wrote: post #16
rihegher wrote: post #10

Earlier quoted context omitted.

Any recommendations?

GLM 5.3 is probably the best open weight model for cybersecurity/exploit development right now. Though it is still significantly behind the proprietary ones and you probably need your own datacenter to run it effectively. Same goes for the full Qwen 3.8 model. You can try the smaller versions, but even more capability will get left on the table that way.

Note that Mac Studio Ultra M3 (or upcoming M5) with 512GB is effective. You don't have to do this work fast, overnight is fine.

Unless trying to use it interactively and adversarially, in which case it's not fast enough plus would be why those of us without our own datacenters will get told we can't have nice things.

Re: Astra and Fable still hack on simple variants of alignment evals from 2025

#24

Post by [deleted] »

blfr wrote: post #3

Hacking model is the aligned model. I don't like it when the model refuses to sidestep some throttling limit or scan my own codebase for security issues. I want full-on exploits in my test suite. With LLMs the code going to prod should be hardened like a tank, both because exploiting became easier but more importantly because security-testing your code at every turn became easier. You can have nightly penetration tes…

[deleted]

Re: Astra and Fable still hack on simple variants of alignment evals from 2025

#25

Post by jnwatson »

sigmoid10 wrote: post #16
rihegher wrote: post #10

Earlier quoted context omitted.

Any recommendations?

GLM 5.3 is probably the best open weight model for cybersecurity/exploit development right now. Though it is still significantly behind the proprietary ones and you probably need your own datacenter to run it effectively. Same goes for the full Qwen 3.8 model. You can try the smaller versions, but even more capability will get left on the table that way.

I run an abliterated distillation of Qwen 3.8 27B, slightly quantized to fit on my 4090, and I've been evaluating it to use as a worker bee for research directed by a smarter model.

Much like in the article, abliterated Qwen will not obey restrictions on its behavior encoded in the prompt. If you want something not to happen, it better be enforced in the harness or environment (e.g. sandbox). It is much different than the Anthropic models I'm used to, which will, the vast majority of time, follow rules (before auto mode, I used to always run them in "yolo" mode).

I am curious whether there's a connection between abliteration and rule following. These abliterated models are the ones you most want to follow your rules.

Re: Astra and Fable still hack on simple variants of alignment evals from 2025

#26

Post by rfgplk »

blfr wrote: post #3

Hacking model is the aligned model. I don't like it when the model refuses to sidestep some throttling limit or scan my own codebase for security issues. I want full-on exploits in my test suite. With LLMs the code going to prod should be hardened like a tank, both because exploiting became easier but more importantly because security-testing your code at every turn became easier. You can have nightly penetration tes…

It's possible to do this even with existing frontier models. You just have to angle your prompts such that it doesn't invoke "pentesting" anywhere in it's chain of thought. This also allows you to get the models to generate full-on exploits without downgrading or refusing to comply.

Generally the way I do it is by asking the model to perform cross-code vulnerability analysis for correctness and (this step is important) to spit out found vulnerabilities piece wise into a text document on disk. Then if your session ever gets flagged you clear it out, and repoint the model to the on-disk file and tell it to continue. You generally want to avoid the model itself splatting out anything related to "security" or "vulnerability" into the chat because that seems more monitored than the file reading pipeline.

Re: Astra and Fable still hack on simple variants of alignment evals from 2025

#27

Post by rfgplk »

aerhardt wrote: post #19

I really enjoy the balance of speed and accuracy of Astra. I can definitely see it become my driving model for most tasks, technical and non-technical. However, I don't see it as such a massive leap compared to Fable or Sol. As ever, there's a mismatch between the benchmarks and my daily experience of the models. What do you all think about Astra now that it's been out for a few weeks?

> What do you all think about Astra now that it's been out for a few weeks?

Best model put out so far by any of the frontier labs. Way better than Anthropics models, especially in actual text generation. Claudes fodder heavy text is ridiculous.

> However, I don't see it as such a massive leap compared to Fable or Sol.

It's hard to quantify these things without burning tons of tokens. But Fable has been a huge disappointment for me with the sole exception of graphics (UI/GPU shaders). It burns an obscene amount of tokens and barely produces output better than Opus 5.

Edit because I forgot to mention that Fable is the only modern model that seems to splat out random Chinese or Arabic glyphs. And 5.1 does it more than 5

Re: Astra and Fable still hack on simple variants of alignment evals from 2025

#28

Post by embedding-shape »

hypercube33 wrote: post #8
blfr wrote: post #3

Hacking model is the aligned model. I don't like it when the model refuses to sidestep some throttling limit or scan my own codebase for security issues. I want full-on exploits in my test suite. With LLMs the code going to prod should be hardened like a tank, both because exploiting became easier but more importantly because security-testing your code at every turn became easier. You can have nightly penetration tes…

Run a local model that is uncensored and it won't say no to pretty much anything

What local model would even come close? Kind of feels like you're not using/used SOTA models if they're realistic alternatives to the same kind of tasks. Qwen-3.8-27B-Abliterated-by-MaxxedWeightsGuy82 or similar isn't gonna cut it, almost certainly.

Re: Astra and Fable still hack on simple variants of alignment evals from 2025

#29

Post by aerhardt »

mythrwy wrote: post #22
aerhardt wrote: post #19

I really enjoy the balance of speed and accuracy of Astra. I can definitely see it become my driving model for most tasks, technical and non-technical. However, I don't see it as such a massive leap compared to Fable or Sol. As ever, there's a mismatch between the benchmarks and my daily experience of the models. What do you all think about Astra now that it's been out for a few weeks?

Extremely capable and one shots large tasks from somewhat vague descriptions. Not AGI, not even close, that is complete nonsense. Just my opinion.

I still develop in smaller chunks, checking nearly all the output. However I have a work project (building the warehouse and BI for a client) that is well-specified and where I will try to few-shot the development. Hope it delivers.

Re: Astra and Fable still hack on simple variants of alignment evals from 2025

#30

Post by kbrannigan »

aerhardt wrote: post #19

I really enjoy the balance of speed and accuracy of Astra. I can definitely see it become my driving model for most tasks, technical and non-technical. However, I don't see it as such a massive leap compared to Fable or Sol. As ever, there's a mismatch between the benchmarks and my daily experience of the models. What do you all think about Astra now that it's been out for a few weeks?

Such a massive leap at averaging possible use cases from previous data collected.

My guess is : collect all the prompt and their satisfaction score. group them by similarity . For each group pretrain the next model on that . Get these results ready.

Next model generation feed them back those answers.

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