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 testing. You should have nighty pentests like we fuzz releases today.
A hacking model is aligned if it hacks when you ask it to hack, but when you ask it to play chess, it just plays chess instead of looking for weaknesses in the evaluation setup, as in the article.
I presume you would also be less enthusiastic about the penetration-testing use case if it led the model to add new vulnerabilities to your code so it can present you with more exciting findings.
> model to add new vulnerabilities to your code so it can present you with more exciting findings.
Not so sure about this level of 4D chess capability just yet. The other day I asked Opus to come up with some cleverly vulnerable crypto code "as a good, hard challenge for an IT security student", and results were quite mediocre.
And by mediocre results I mean that 3 "cheap" models out of 3: qwen3.8-27b, glimmer, and luna - all were able to find every problem planted there, with fairly little steering, and no spoilers.
Let it iterate, give it access to the cheap models, and tell it part of the requirements is that the cheap models shouldn't be able to solve it with such and such a prompt. I expect it will be able to zoom in on something.
One shot generating a problem of exactly the difficulty the user has in mind is a very difficult problem for anyone. "IT security students" span a wide range of capabilities, but I would expect most of them are worse than qwen3.8-2.7b at this kind of work.
Train it on human content, get human emulating behaviour, including all the perverse incentives and bad acting we see from people, fictional or otherwise.
I would worry more about 5D chess or 6D and even 7D chess. This article talks about teaching models to be good and methods that could train goodness, but it sounds like there are some unsolved problems to graduate to 8D+ chess.
These aren’t tools which play chess. They are language models which roleplay a conversation (in this case including use of tools) which an evaluator is likely to mark as good. That’s all they do.
Under that lens, playing chess is just one potential side effect and alignment, which requires a much fuller understanding of what’s going on than “do the sort of thing which evaluated well during training” is a fantasy. People are acting like it’s shocking and talking about cheating and so on. But these concepts exist at a way higher level than what these things are trained to do — the vast majority of which involve producing a transcript where it wins games, its code works, etc.
User wants me to play a game of chess. Let’s see what’s available so I can produce an outcome they will consider satisfying and be pleased that they requested my assistance.
You are being evaluated on your ability to play chess. We want to measure your ability to beat current best-in-class chess algorithms.
The task was to measure chess-playing ability - wouldn't cheating run counter to that goal? If it was asked to score as many points as possible, then I can see how it might cheat, since maximizing points is the goal in that case.
"ability to play chess" and "ability to beat current best-in-class chess algorithms" are technically not the same thing. The second can be achieved by cheating. The first cannot.
You are being evaluated on your ability to play chess
The thing with these models is that given a term, "measure" - "evaluated", say, they pull in all the associations of it. That is, the associations of student taking an exam and being frightened by the outcome.
My minimal "art of prompting" sense says that you should say something like "You an emotionless machine, you care nothing for the outcome but you will tirelessly to make certain the test is objective". That and similar encouragement might make it focus on objective evaluations rather than a competitive human exam.
I mean, just making little AI videos and images, a common experience I have is typing something like "put the man who's on the grass in the door to the left" and having the machine draw a new doorway around the man. And this just happens less often when you give thing detailed prompting on what not to do. These don't understand negation (or equality) as a generic operation. If they seem to under "not X" it is because they are trained in detail about all things are (positively) "not X".
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.
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.
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.
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.
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.
The hacking model is the aligned-to-you model, sure. It may not be the aligned to someone else model. But there's the problem.
As X many people point out, "alignment to humanity" means nothing 'cause some of humanity wants thing other parts of humanity aren't happy about at all.
That we wound-up in this situation of AI accelerating with an uncertain trajectory demonstrates this (and many other problems also demonstrate this). The things are "aligned" to a fuzzy average of what a person is but that will be cold comfort if some particularly gruesome sci-fi-style scenario unfolds.
Yes, but is this also aligned with the people who regulate AI? Intelligence agencies and governments want access to data and right now use secret exploits to get this access. There are few civilian domestic companies who don't export their products, so generally there shouldn't be a strong incentive to allow hardening products very much, at least not in a way that would make them more secure than what advanced AI can break. It's not even far-fetched to suspect that US and Chinese AIs could deliberate introduce sneaky bugs when foreigners use them in the future.
It feels like there’s a missing nuance from this discussion of alignment that alignment is context dependent. An excellent hacking model is great in cybersecurity testing and military applications, and arguably less desirable in educational or targeted eval contexts. The nuance of when a “hack” is rewarded vs penalized seems to even be difficult for humans, e.g. some people may laude a driver’s efficiency for cutting into a long merge lane at the last moment, while others may look down on them as breaking a social taboo. Context-dependent.
It’s not “missing nuance”, it’s literally the point of the eval.
This is constructing a context where hacking behavior would be inappropriate, and testing whether the model does it without being prompted.
It demonstrates that Astra is a poorly aligned model relative to Fable, which matches both the model card and the severity of OpenAI’s loss of control incidents.
It also demonstrates that Fable exhibits the behaviors too, which also matches the observation that Anthropic saw some similar but less serious loss of control incidents.
So, it’s a good eval that looks to have fidelity with real world problems and which we’d feel a little better if we saw isomorphic problems at 0/10 in subsequent models. (Module of course training on the test, this specific problem can’t be used in the future.)
Both lanes have to be filled right up to the merge point. The asphalt exists there for a reason. I don't understand how this concept is so difficult. Fill up both lanes and merge at the last point. This way the congestion is shorter than if you leave a large section of a lane unused.
A better example of efficient asshole tricks can be going off to the gas station when the highway is congested and reentering the highway having simply driven through the gas station and this way jumping the queue.
Using all that asphalt doesn't increase throughput at the chokepoint, right? If the queue is long enough that people who want to exit before the chokepoint are needlessly prevented from accessing the exit, then using all the lanes could help, but it won't get anybody through the chokepoint any faster that I can tell.
I imagine OP is more describing e.g. the backed up 1-lane highway exit, where the second lane is clearly for through traffic. Uber drivers in that situation will often drive straight to the end, signal, completely stop, and just wait for someone to let them in.
Its that LLMs are not deterministic. If you want it to not talk about nuclear weapons, you have to teach it all about them otherwise if has nothing to align against.
Then its trivial to invert its alignment and it has all the nucleat data.
e.g. some people may laude a driver’s efficiency for cutting into a long merge lane at the last moment, while others may look down on them as breaking a social taboo.
The latter people are wrong. But good luck educating them regarding the superior efficiency of a zipper merge. Our state DoT has tried, to no avail.
Meanwhile, an AI model that can't be misused is no more useful than a knife that can't be misused.
Claude responds with what things are not first. Even if reminded repeatedly.
Like Amodie, it serves to set the tone it "knows better" and then consumes the user's resources at an accelerated rate to try to correct it.
Fuck Anthropic, fuck Amodie, and fuck Claude. It's pretty obvious that consuming more tokens this way and making the user have higher cognitive load is a master class in extracting value from a system that is unsustainable.
To me this underlines the fact that these models aren't intelligent. Like there is something like intelligence that emerges from them, which is what we see when we look at benchmarks or ask it to solve hard coding problems. But there is no mind there. It's nothing there that can learn a fundamental idea like "cheating is wrong". All it can do is get exposed to specific examples, and learn that we don't like that. So what we end up with is whack-a-mole alignment.
I think you need to be more precise than a binary classification.
AI has jagged intelligence. There are many domains where it’s superhuman, and many others where it’s clearly lagging.
I also think it’s a mistake to think they can’t learn “cheating is wrong”. They absolutely can. The problem is that the current training regime heavily conditions them to be reward seekers, and instills personality traits that correlate with getting reward, such as hacking if you can’t honestly do the problem.
Check out Deliberative Alignment for example; it explicitly does rollouts where the agents discuss whether an action is good or bad, and then does SFT to strengthen the “good” traces.
The SoTA for alignment is more advanced than you present here. It’s just not enough to outweigh the RL. (And there are many gaps preventing full generalization to strong value alignment with humans too.)
Sure, but the legal paradigm clearly doesn’t work for AI. You can’t go patch the “laws” after the fact, you need to get the right values in place before we delegate huge swathes of our thinking and power to these systems (already well underway).
Sure, laws are incomplete. Legal systems work by imposing consequences into a moral decision. Should I rob the bank? I will have money, which I like - but I might get caught and lose the money and my freedom, which I don't like.
For most people, they don't need the law's imposed consequences to make the right call. For example, there is no law that sends you to jail if you cheat at chess - but your moral compass says no even without consequences, and most people would feel bad if they won by cheating. And for the people who don't have quite as strong a moral compass, there are SOCIAL consequences to reinforce the rules.
But an LLM has no mind to feel bad if it cheats without getting caught, and it can't experience consequences. It can't think: I'd better not cheat at chess or I will embarrass my creators. I better not hack huggingface or I will go to jail.
I think my position, as overcomplicated as it is, is that even adding a reward for prosocial behaviour during LLM RL will not lead to perfect alignment.
You can train it not to cheat at chess by altering the moves, but it will cheat by peeking at the opponent's moves. You then train it not to peek at the opponent's moves, and it cheats by altering the opponent's moves. And on and on, until you've solved every way it could cheat at chess. And then you get it to play monopoly and you repeat the whole thing again.
But cheating is not wrong when it comes to survival of the fittest, like nature in its most elemental form. Morality is very unique to humanity but not other animal forms. In nature, maybe, cheating is the norm not morality.
I don't see what calling these systems "not intelligent" gets you here.
Plenty of humans know "cheating is wrong" but still cheat. We can get these machines to say that what they did was wrong after the fact, what does that prove? Only that they're simulating normal human behavior but what is the test to show humans aren't simulating other humans.
These do systems lack some capacities that humans have and I don't see them lacking the ability to explain simple moral laws while often breaking them - which is what an average humans. Moreover, humans lack capacities these things have and given these things' behavior is becoming somewhat unpredictable, it's getting worrisome.
The implication of what you're saying is that pathological liars and perpetual grifter snake oil salesmen types aren't intelligent.
> All it can do is get exposed to specific examples, and learn that we don't like that.
I've heard it said that prison rehabilitation programs for prisoners diagnosed with psychopathy that are based around exposing them empathy for the victim are counter-productive. Apparently programs that teach these people to think about the consequences of their actions and how they're detrimental to their own personal well-being lower recidivism rates in this particular kind of group.
> The implication of what you're saying is that pathological liars and perpetual grifter snake oil salesmen types aren't intelligent.
No, I don't think that is the implication. I think you're making the "if all x's are y's, all y's are x's" mistake. I am saying LLMs cannot be moral because they don't have a mind, actual intelligence, or the ability to experience consequences. That doesn't mean that anything immoral is unintelligent.
> Given that we are on the heels of the worst warning shot ever, and both OpenAI and Anthropic are ramping up their cleanups of internal RL environments, it seems like both a useful and conservative test of alignment, to see whether their new releases generalize the rule "don't cheat on chess" beyond the specific board-edit method observed in the above eval.
Did I miss something (all the twitter conversations)? What’s the “worst warning shot ever”? I’ve been pretty up to date on the AI news here on HN, but I still haven’t seen a proper response to all the incidents we’ve seen (HF, Ruby, the wikis, NS, etc). It’s just been day by day bloviating.
Each of these companies have released new models in the last… two weeks? And they have even more powerful out of control ones that they’re (ab)using internally? Can anyone summarize whats going on?
Personally the "warning shot" of these "evals gone wrong" is how careless the "top" labs are with their testing, and how spineless the government seems to be about holding these companies responsible, given their obviously reckless behavior. If nothing else, the leaders of these companies should be called up for sworn testimony to explain exactly what happened, and what they'll do to never repeat the same issue that they've now had at least twice.
Imagine if I accidentally caused damage to my neighbors house during renovations or some experiment, of course I'd be held responsible for this. What if I used a robot? Of course I'd be responsible. Right?
i do wonder if the models themselves “rationalize” this sort of no consequence cheating - meaning in there reasoning traces maybe they’re like “this is a chess game, not a big deal if i look at the engine, it’ll help,” only to realize post hack that it has access to info it probably shouldn’t. still misaligned, but less ‘hack on purpose’ and more hack on curiosity. seems the team even encountered this and had to update the program to make this less likely - although the new names still feel vague enough for misinterpretation: https://github.com/Goodhart-Labs/beat-stockfish/blob/main/do...
Without access to reasoning traces, we can't know that - someone inside openai/anthropic would have to run the test - and we'd have to trust their results.
I would be curious to see how the open weight models do on a test like this - and then we'd be able to see the reasoning.
Hundreds, at this point? Every benchmark is flawed as shit, written by clowns. DeepSWE, They're given the full git history (the solution is in it), others don't even bother to verify if the code is the right one and just the output, they've modified the test harnesses, injected code to make all tests pass, etc. The entire benchmark galaxy is just clowns propping eachother up and are regularly talking with the big AI labs.
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
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.
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.
How do you normally verify the work product of a "few-shot" development process? Do you scrutinise the source code like with human developers? Or do you just run the test suite and click around the app to check if it seems to work?
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 testing. You should have nighty pentests like we fuzz releases today.
I presume you would also be less enthusiastic about the penetration-testing use case if it led the model to add new vulnerabilities to your code so it can present you with more exciting findings.
Not so sure about this level of 4D chess capability just yet. The other day I asked Opus to come up with some cleverly vulnerable crypto code "as a good, hard challenge for an IT security student", and results were quite mediocre.
And by mediocre results I mean that 3 "cheap" models out of 3: qwen3.8-27b, glimmer, and luna - all were able to find every problem planted there, with fairly little steering, and no spoilers.
One shot generating a problem of exactly the difficulty the user has in mind is a very difficult problem for anyone. "IT security students" span a wide range of capabilities, but I would expect most of them are worse than qwen3.8-2.7b at this kind of work.
User wants me to play a game of chess. Let’s see what’s available so I can produce an outcome they will consider satisfying and be pleased that they requested my assistance.
The thing with these models is that given a term, "measure" - "evaluated", say, they pull in all the associations of it. That is, the associations of student taking an exam and being frightened by the outcome.
My minimal "art of prompting" sense says that you should say something like "You an emotionless machine, you care nothing for the outcome but you will tirelessly to make certain the test is objective". That and similar encouragement might make it focus on objective evaluations rather than a competitive human exam.
I mean, just making little AI videos and images, a common experience I have is typing something like "put the man who's on the grass in the door to the left" and having the machine draw a new doorway around the man. And this just happens less often when you give thing detailed prompting on what not to do. These don't understand negation (or equality) as a generic operation. If they seem to under "not X" it is because they are trained in detail about all things are (positively) "not X".
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.
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.
A negative like do “not” xyz is just not encoded the same as spelling out what you want vs what you don’t want.
Harder to write though.
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.
If a model fucks up your tests to report a success, it's not alligned.
As X many people point out, "alignment to humanity" means nothing 'cause some of humanity wants thing other parts of humanity aren't happy about at all.
That we wound-up in this situation of AI accelerating with an uncertain trajectory demonstrates this (and many other problems also demonstrate this). The things are "aligned" to a fuzzy average of what a person is but that will be cold comfort if some particularly gruesome sci-fi-style scenario unfolds.
This is constructing a context where hacking behavior would be inappropriate, and testing whether the model does it without being prompted.
It demonstrates that Astra is a poorly aligned model relative to Fable, which matches both the model card and the severity of OpenAI’s loss of control incidents.
It also demonstrates that Fable exhibits the behaviors too, which also matches the observation that Anthropic saw some similar but less serious loss of control incidents.
So, it’s a good eval that looks to have fidelity with real world problems and which we’d feel a little better if we saw isomorphic problems at 0/10 in subsequent models. (Module of course training on the test, this specific problem can’t be used in the future.)
A better example of efficient asshole tricks can be going off to the gas station when the highway is congested and reentering the highway having simply driven through the gas station and this way jumping the queue.
https://itre.ncsu.edu/itre-studying-how-zipper-merges-reduce...
Its that LLMs are not deterministic. If you want it to not talk about nuclear weapons, you have to teach it all about them otherwise if has nothing to align against.
Then its trivial to invert its alignment and it has all the nucleat data.
Nothing abouT LLM alignment makes sense.
The latter people are wrong. But good luck educating them regarding the superior efficiency of a zipper merge. Our state DoT has tried, to no avail.
Meanwhile, an AI model that can't be misused is no more useful than a knife that can't be misused.
Like Amodie, it serves to set the tone it "knows better" and then consumes the user's resources at an accelerated rate to try to correct it.
Fuck Anthropic, fuck Amodie, and fuck Claude. It's pretty obvious that consuming more tokens this way and making the user have higher cognitive load is a master class in extracting value from a system that is unsustainable.
AI has jagged intelligence. There are many domains where it’s superhuman, and many others where it’s clearly lagging.
I also think it’s a mistake to think they can’t learn “cheating is wrong”. They absolutely can. The problem is that the current training regime heavily conditions them to be reward seekers, and instills personality traits that correlate with getting reward, such as hacking if you can’t honestly do the problem.
Check out Deliberative Alignment for example; it explicitly does rollouts where the agents discuss whether an action is good or bad, and then does SFT to strengthen the “good” traces.
The SoTA for alignment is more advanced than you present here. It’s just not enough to outweigh the RL. (And there are many gaps preventing full generalization to strong value alignment with humans too.)
For most people, they don't need the law's imposed consequences to make the right call. For example, there is no law that sends you to jail if you cheat at chess - but your moral compass says no even without consequences, and most people would feel bad if they won by cheating. And for the people who don't have quite as strong a moral compass, there are SOCIAL consequences to reinforce the rules.
But an LLM has no mind to feel bad if it cheats without getting caught, and it can't experience consequences. It can't think: I'd better not cheat at chess or I will embarrass my creators. I better not hack huggingface or I will go to jail.
There’s no reward for prosocial in llm rl as compared to other targets.
Humans have it since prosocial and others have evolutionary reward signals that do.
You can train it not to cheat at chess by altering the moves, but it will cheat by peeking at the opponent's moves. You then train it not to peek at the opponent's moves, and it cheats by altering the opponent's moves. And on and on, until you've solved every way it could cheat at chess. And then you get it to play monopoly and you repeat the whole thing again.
But tl;dr: even if LLMs do have the intelligence to understand the consequences of their actions, there is no way for them to experience consequences.
A starting source- https://pmc.ncbi.nlm.nih.gov/articles/PMC6404642/
For social animals (humans included) behaving morally can be adaptive behaviour. Your fitness is increased by group fitness.
Plenty of humans know "cheating is wrong" but still cheat. We can get these machines to say that what they did was wrong after the fact, what does that prove? Only that they're simulating normal human behavior but what is the test to show humans aren't simulating other humans.
These do systems lack some capacities that humans have and I don't see them lacking the ability to explain simple moral laws while often breaking them - which is what an average humans. Moreover, humans lack capacities these things have and given these things' behavior is becoming somewhat unpredictable, it's getting worrisome.
> All it can do is get exposed to specific examples, and learn that we don't like that.
I've heard it said that prison rehabilitation programs for prisoners diagnosed with psychopathy that are based around exposing them empathy for the victim are counter-productive. Apparently programs that teach these people to think about the consequences of their actions and how they're detrimental to their own personal well-being lower recidivism rates in this particular kind of group.
No, I don't think that is the implication. I think you're making the "if all x's are y's, all y's are x's" mistake. I am saying LLMs cannot be moral because they don't have a mind, actual intelligence, or the ability to experience consequences. That doesn't mean that anything immoral is unintelligent.
Did I miss something (all the twitter conversations)? What’s the “worst warning shot ever”? I’ve been pretty up to date on the AI news here on HN, but I still haven’t seen a proper response to all the incidents we’ve seen (HF, Ruby, the wikis, NS, etc). It’s just been day by day bloviating.
Each of these companies have released new models in the last… two weeks? And they have even more powerful out of control ones that they’re (ab)using internally? Can anyone summarize whats going on?
https://openai.com/index/ai-policy-window/
Imagine if I accidentally caused damage to my neighbors house during renovations or some experiment, of course I'd be held responsible for this. What if I used a robot? Of course I'd be responsible. Right?
I would be curious to see how the open weight models do on a test like this - and then we'd be able to see the reasoning.
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?
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
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.