Meta got caught gaming AI benchmarks
170 comments
·April 8, 2025Mond_
The Llama 4 launch looks like a real debacle for Meta. The model doesn't look great. All the coverage I've seen has been negative.
This is about what I expected, but it makes you wonder what they're going to do next. At this point it looks like they are falling behind the other open models, and made an ambitious bet on MoEs, without this paying off.
Did Zuck push for the release? I'm sure they knew it wasn't ready yet.
throwaway25251
I don't know about Llama 4. Competition is intense in this field so you can't expect everybody to be number 1. However, I think the performance culture at Meta is counterproductive. Incentives are misaligned, I hope leadership will try to improve it.
Employees are encouraged to ship half-baked features and move to another project. Quality isn't rewarded at all. The recent layoffs have made things even worse. Skilled people were fired, slowing down teams. I assume the goal was to push remaining employees to work even more, but I doubt this is working.
I haven't worked in enough companies of this size to be able to tell if alternatives are better, but it's very clear to me that Meta doesn't get the best from their employees.
a4isms
For those who haven't heard of it, "The Hawthorne Effect" is the name given to a phenomena where when a person or group being studied is aware they are being studied, their performance goes up but as much as 50% for 4-8 weeks, then regresses to its norm.
This is true if they are just being observed, or if some novel new processes are introduced. If the new things are beneficial, the performance rises for 4-8 weeks as usual, but when it regresses it regresses to a higher performance reflecting the value of the new process.
But when poor management introduce a counter-productive change, the Hawthorne Effect makes it look like a resounding success for 4-8 weeks. Then the effect fades, and performance drops below the original level. Sufficiently devious managers either move on to new projects or blame the workers for failing to maintain the new higher pace of performance.
This explains a lot of the incentive for certain types of leaders to champion arbitrary changes, take a victory lap, and then disassociate themselves from accountability for the long-term success or failure of their initiative.
(There is quite a bit of controversy over what the mechanisms for the Hawthorne Effect are, and whether change alone can introduce it for whether participants need to feel they are being observed, but the model as I see it fits my anecdotal experience where new processes are always accompanied by attempts to meet new performance goals, and everyone is extremely aware that the outcome is being measured.)
1659447091
> There is quite a bit of controversy over what the mechanisms for the Hawthorne Effect are, and whether change alone can introduce it for whether participants need to feel they are being observed
My vote is change alone can introduce it; going from not being observed to observed is a change itself. People get into a daily groove after adjusting to whatever circumstance or process (maybe that averages 4-8 weeks I have no idea), but introduce a novel thing into that and they "perk up" until that bit leaves or integrates into the daily groove. In my experience, people prefer working on new features, even if that feature is some managements arbitrary initiative. They rather work on that new ridiculous thing than continue on their previous slog through the bug backlog until the new thing becomes a slog itself.
pixl97
I mean, it sounds like we should add in the McNamara fallacy also.
berkes
I've never liked it, but
> Move fast and break things
is really a bad concept in this space, where you get limited shots at releasing something that generates interest.
> Employees are encouraged to ship half-baked features
And this is why I never liked that motto and have always pushed back at startups where I was hired that embraced this line of thought. Quality matters. It's context-dependent, so sometimes it matters a lot, and sometimes hardly. But "moving fast and breaking things" should be a deliberate choice, made for every feature, module, sprint, story all over again, IMO. If at all.
diggan
> is really a bad concept in this space, where you get limited shots at releasing something that generates interest.
Sure, but long term effects are more depending on the actual performance of the model, than anything.
Say they launch a model that is hyped to be the best, but when people try it, it's worse than other models. People will quickly forget about it, unless it's particularly good at something.
Alternatively, say they launch a model that doesn't even get a press release, or any benchmark results published ahead of launch, but the model actually rocks at a bunch of use cases. People will start using it regardless of the initial release, and continue to do so as long as it's a best model.
Rebuff5007
I'd argue its a bad concept in any spaces that involve teams of people working together and deliverables that enter the real world.
Lutger
I'm with you. Yet I've always understood 'move fast and break things' to mean that there is value in shipping stuff to production that is hard to obtain just sitting in the safe and relatively simple corner of your local development environment, polishing up things for eternity without any external feedback whatsoever, months or even years, and then doing a big drop. That's completely orthogonal to things like planning, testing, quality, taking time to think etc.
Maybe the fact that this is how I understood that motto is in itself telling.
lenerdenator
> is really a bad concept in this space, where you get limited shots at releasing something that generates interest.
It's a really bad concept in any space.
We would be living in a better world if Zuck had, at least once, thought "Maybe we shouldn't do that".
spaceywilly
I agree. I think of it like a car engine. You can push it up to a certain RPM and it will keep making more and more power. Above that RPM, the engine starts to produce less power and eventually blows a gasket.
I think the performance-based management worked for a while because there were some gains to be had by pushing people harder. However, they’ve gone past that and are now pushing people too hard and getting worse results. Every machine has its operating limits and an area where it operates most efficiently. A company is no different.
null
lenerdenator
The problem is, there's always some engine pushing the power envelope, or a person pushing their performance harder. And then the rest of them have to keep up.
hq123
very nice analogy!
philjohn
It's 100% PSC (their "Performance Culture")
You're not encouraged per se to ship half-baked features, but if you don't have enough "impact" at the end of the half (for mid cycle checkin) or year (for full PSC cycle) then you're going to get "Below Expectations" and then "Meets Most" (or worse) and with the current environment a swift offboarding.
When I was there (working in integrity) our group of staff+ engineers opined how it led to perverse incentives - and whilst you can work there and do great work, and get good ratings, I saw too many examples of "optimizing for PSC" (otherwise known as PSC hacking).
_bin_
It's also terrible output, even before you consider what looks like catastrophic forgetting from crappy RL. The emoji use and writing style make me want to suck-start a revolver. I don't know how they expect anyone to actually use it.
hintymad
> Employees are encouraged to ship half-baked features and move to another project
Maybe there is more to that. It's been more than a year since Llama 3 was released. That should be enough time for Meta to release something with significantly improvement. Or you mean quarter by quarter the engineers had to show that they were making impact in their perf review, which could be detrimental to the Llama 4 project?
Another thing that puzzles me is that again and again we see that the quality of a model can improve if we have more high-quality data, yet can't Meta manage to secure massive amount of new high-quality data to boost their model performance?
magixx
> Or you mean quarter by quarter the engineers had to show that they were making impact in their perf review
This is what I think they were referencing. Launching things looks nice in review packets and few to none are going to look into the quality of the output. Submitting your own self review means that you can cherry pick statistics and how you present them. That's why that culture incentivizes launching half baked products and moving on to something else because it's smart and profitable (launch yet another half baked project) to distance yourself from the half baked project you started.
alfalfasprout
Yep they've basically created a culture where people are incentivized to look busy, ship things fast, and look out for themselves. Which attracts and retains people that thrive in that kind of environment.
That's a terrible way to execute on big, ambitious projects since it discourages risky bets and discourages collaboration.
root_axis
It's not a big deal. Llama 4 feels like a flop because the expectations are really high based on their previous releases and the sense of momentum in the ecosystem because of DeepSeek. At the end of the day, LLama 4 didn't meet the elevated expectations, but they're fine. They'll continue to improve and iterate and maybe the next one will be more hype worthy, or maybe expectations will be readjusted as the specter of diminishing returns continues to creep in.
int_19h
It feels like a flop because it is objectively worse than models many times smaller that shipped sometimes ago. In fact, it is worse than earlier LLaMA releases on may points. It's so bad that people who initially ran into it assumed that the downloaded weights must be corrupted somehow.
lerchmo
The switching costs are so low (zero) that anyone using these models just jumps to the best performer. I also agree that this is not a brand or narrative sensitive project.
qoez
> it makes you wonder what they're going to do next
They're just gonna keep throwing money at it. This is a hobby and talent magnet for them, instagram is the money printer. They've been working on VR for like a decade with barely much results in terms of users (compared to costs). This will be no different.
wongarsu
Both are also decent long-terms bets. Being VR market leader now means they will be VR market-leader with plenty of inhouse talent and IP when the technology matures and the market grows. Being in the AI race, even if they are not leading, means they have in-house talent and technology to be able to react to wherever the market is going with AI. They have one of the biggest messengers and one of the biggest image-posting sites, there is a decent chance AI will become important to them in some not-yet-obvious way.
One of Meta's biggest strengths is Zuckerberg being able to play these kinds of bets. Those bets being great for PR and talent acquisition is the cherry on top
TheOtherHobbes
This assumes no upstart will create a game changing innovation which upends everything.
Companies become complacent and confused when they get too big. Employees become trapped in a maze of performative careerism, and customer focus and a realistic understanding of threats from potential competitors both disappear.
It's been a consistent pattern since the earliest days of computing.
Nothing coming out of Big Tech at the moment is encouraging me to revise that assumption.
ntlm1686
"made an ambitious bet on MoEs"? No, DeepSeek is MoE, and they succeeded. Meta is not betting on MoE, it just does what other people have done.
antirez
Llama4 seems in many ways a cut and paste of DeepSeek. Including the shared expert and the high sparsity. It's a DeepSeek that does not work well.
bko
I remember reading that they were in panic mode when the DeepSeek model came out so they must have scrambled and had to re-work a lot of things since DeepSeek was so competitive and open source as well
blueboo
Fear of R2 looms large as well. I suspect they succumbed to the nuance collapse along the lines of “Is double checking results worth it if DeepSeek eats our lunch?”
overfeed
> Did Zuck push for the release?
All I know is it is the first Llama release since Zuck brought "masculine energy" back to Meta.
agilob
They knew they can't beat DeepSeek 2 months ago https://old.reddit.com/r/LocalLLaMA/comments/1i88g4y/meta_pa...
nightski
Do you know that they made a bet on MoE? Meaning they abandonded dense models? I doubt that is the case. Just releasing MoE Llama 4 does not constitute a "bet" without further information.
Also from what I can tell this performs better than models with parameter counts equal to one expert, and worse than fully dense models equal to total parameter count. Isn't that kind of what we'd expect? in what way is that a failure?
Maybe I am missing some details. But it feels like you have an axe to grind.
genewitch
A 4x8 MOE performs better than an 8B but worse than a 32B, is your statement?
My response would be, "so why bother with MOE?"
However deepseek r1 is MOE from my understanding, but the "E" are all =>32B parameters. There's > 20 experts. I could be misinformed; however, even so, I'd say a MOE with 32B or even 70B experts will outperform (define this!) Models with equal parameter counts, because deepseek outperforms (define?) ChatGPT et al.
zamadatix
DeepSeek V3/R1 are MoE with 256 experts per layer, actively using 1 shared expert and 8 routed experts per layer https://arxiv.org/html/2412.19437v1#S2:~:text=with%20MoE%20l... so you can't just take the active parameters and assume that's close to the size of a single expert (ignoring experts are per layer anyways and that there are still dense parameters to count).
Despite connotations of specialized intelligences the term "expert" provokes it's really mostly about scalability/efficiency of running large models. By splitting up sections of the layers and not activating all of them for each pass a single query takes less bandwidth, can be distributed across compute, and can be parallelized with other queries on the same nodes.
nightski
Easy, vastly improved inference performance on machines with larger RAM but lower bandwidth/compute. These are becoming more popular such as Apple's M series chips, AMD's strix halo series, and the upcoming DGX Spark from Nvidia.
LetsGetTechnicl
I'm just shocked that the companies who stole all kinds of copyrighted material would again do something unethical to keep the bubble and gravy train going...
antonkar
Yes, their worst fear is people figuring out that an AI chatbot is a strict librarian that spits out quotes but doesn't let you enter the library (the AI model itself). Because with 3D game-like UIs people can enter the library and see all their stolen personal photos (if they were ever online), all kind of monsters. It'll be all over YouTube.
Imagine this but you remove the noise and can walk like in an art gallery (it's a diffusion model but LLMs can be loosely converted into 3D maps with objects, too): https://writings.stephenwolfram.com/2023/07/generative-ai-sp...
djmips
Do you really think facebook's model weights contain all the facebook personal photos?
antonkar
With more than 50% probability. Instagram has a clause that they can use your photos for ads and endorsements, basically for profit.
Many sites repost those photos, I doubt Meta will care to meticulously remove them. If the model can generate photo-like content of people - it had photos of people put inside.
I doubt they only trained on public domain photos of people.
If they started doing shady stuff, why would they ever stop?
ekojs
I think it's most illustrative to see the sample battles (H2H) that LMArena released [1]. The outputs of Meta's model is too verbose and too 'yappy' IMO. And looking at the verdicts, it's no wonder by people are discounting LMArena rankings.
[1]: https://huggingface.co/spaces/lmarena-ai/Llama-4-Maverick-03...
smeeth
In fairness, 4o was like this until very recently. I suspect it comes from training on COT data from larger models.
ed
Yep, it’s clear that many wins are due to Llama 4’s lowered refusal rate which is an effective form of elo hacking.
etamponi
Meta got caught _first_.
davidcbc
Not even first, OpenAI got caught a while back
hooloovoo_zoo
People have been gaming ML benchmarks as long as there have been ML benchmarks. That's why it's better to see if other researchers are incorporating a technique into their actual models rather than 'is this paper the bold entry in a benchmark table'. But it takes longer.
jandrese
When a measure becomes a target it is no longer a good measure.
These ML benchmarks were never going to last very long. There is far too much pressure to game them, even unintentionally.
Mond_
Do you have a source for this? That's interesting (if true).
davidcbc
They got the dataset from Epoch AI for one of the benchmarks and pinky swore that they wouldn't train on it
https://techcrunch.com/2025/01/19/ai-benchmarking-organizati...
wongarsu
It happens with basically all papers on all topics. Benchmarks are useful when they are first introduced and used to measure things that were released before the benchmark. After that their usefulness rapidly declines.
mkolodny
“Got caught” is a misleading way to present what happened.
According to the article, Meta publicly stated, right below the benchmark comparison, that the version of Llama on LMArena was the experimental chat version:
> According to Meta’s own materials, it deployed an “experimental chat version” of Maverick to LMArena that was specifically “optimized for conversationality”
The AI benchmark in question, LMArena, compares Llama 4 experimental to closed models like ChatGPT 4o latest, and Llama performs better (https://lmarena.ai/?leaderboard).
JKCalhoun
Is LMArena junk now?
I thought there was an aspect where you run two models on the same user-supplied query. Surely this can't be gamed?
> “optimized for conversationality”
I don't understand what that means - how it gives it an LMArena advantage.
jsnell
There are almost certainly ways to fine-tune the model in ways that make it perform better on the Arena, but perform worse in other benchmarks or in practice. Usually that's not a good trade-off. What's being suggested here is that Meta is running such a fine-tuned version on the Arena (and reporting those numbers) while running models with different fine-tuning on other benchmarks (and reporting those numbers), while giving the appearance that those are actually the same models.
gwern
It can be easily gamed. The users are self-selected, and they have zero incentive to be honest or rigorous or provide good responses. Some have incentives the opposite way. (There was a report of a prediction market user who said they had won a market on Gemini models by manipulating the votes; LMArena swore furiously there had definitely been no manipulation but was conspicuously silent on any details.) And the release of more LMArena responses has shown that a lot of the user ratings are blatantly wrong: either they're basically fraudulent, or LMArena's current users are people whose ratings you should be optimizing against because they are so ignorant, lazy, and superficial.
At this point, when I look at the output from my Gemini-2.5-pro sessions, they are so high quality, and take so long to read, and check, and have an informed opinion on, I just can't trust the slapdash approach of LMArena in assuming that careless driveby maybe-didn't-even-read-the-responses-ain't-no-one-got-time-for-that-nerd-shit ratings mean much of anything. There have been red flags in the past and I've been taking them ever less seriously even as one of many benchmarks since early last year, but maybe this is the biggest backfire yet. And it's only going to get worse. At this rate, without major overhaul, you should take being #1 on LMArena seriously as useful and important news - as a reason to not use a model.
It's past time for LMArena people to sit down and have some thorough reflection on whether it is still worth running at all, and at what point they are doing more harm than good. No benchmark lives forever and it is normal and healthy to shut them down at some point after having been saturated, but some manage to live a lot longer than they should have...
nhecker
I guess I can't really refute your experience or observations. But, just a single anecdotal point; I use the arena's voting feature quite a bit and I try really hard to vote on the "best" answer. I've got no clue if the majority of the voters put the same level of effort into it or not, but I figure that an honest and rigorous vote is the least I can do in return for a service provided to me free with no obnoxious ads. There's a nonzero incentive to do the right thing, but it's hard to say where it comes from.
As an aside, I like getting two responses from two models that I can compare against one another (and with the primary sources of truth that I know a priori). Not only does that help me sanity-check the responses somewhat, but I get to interact with new models that I wouldn't have otherwise had the opportunity to. Learning new stuff is good, and being earnest is good.
_nick
gwern
In the time it takes you to do one good vote, the low-taste slopmaxxers can do 10 (or 20...?).
light_hue_1
LMArena was always junk. I work in this space and while the media takes it seriously most scientists don't.
Random people ask random stuff and then it measures how good they feel. This is only a worthwhile evaluation if you're Google or Meta or OpenAI and you need to make a chartbot that keeps people coming back. It doesn't measure anything else useful.
genewitch
I hear AI news from time to time from the M5M in the US - and the only place I've ever seen "LMArena" is on HN and in the LM studio discord. At a ratio of 5:1 at least.
brandall10
It's mentioned quite a bit in the LLM related subreddits.
HPsquared
Conversation is a two-way street. A good conversation mechanic could elicit better interaction from the users and result in better answers. Stands to reason, anyway.
cma
Llama 1 derived models on it were beating gpt 3.5 by have less refusals.
jxjnskkzxxhx
In one of karpathys videos he said that he was a bit suspicious that the models that score the highest in LMarena aren't the ones that people use the most to solve actual day to day problems.
consumer451
Tangent, but does anyone know why links to lmarena.ai are banned on Reddit, site-wide?
(Last I checked 1 month ago)
swyx
evidence for this assertion please? its highly unlikely and maybe a bug
consumer451
I should have re-tested prior to posting. It is fixed now. I just tried posting a comment with the link and it was not [removed by reddit].
It was a really strange situation that lasted for months. Posts or comments with a direct link were removed like they were the worst of the worst.
I tried posting to r/bugs and it was downvoted immediately. I eventually contacted he lmarena folks, so maybe they resolved it with reddit.
ignoramous
Ahmad al-Dahle, who leads "Gen AI" at Meta, wrote this on Twitter:
... We're also hearing some reports of mixed quality across different services ...
We've also heard claims that we trained on test sets -- that's simply not true and we would never do that. Our best understanding is that the variable quality people are seeing is due to needing to stabilize implementations.
We believe the Llama 4 models are a significant advancement and we're looking forward to working with the community to unlock their value.
SubiculumCode
There seems to be a lot of haloo, accusations, and rumors, but little meat to any of them. Maybe they rushed the release, were unsure of which one to go with, and some moderate rule bending in terms of which tune got sent to the arena, but I have seen no real hard evidence of real hard underhandedness.
bn-l
I believe this was designed to flatter the prompter more / be more ingratiating. Which is a worry if true (what it says about the people doing the comparing).
add-sub-mul-div
There's no end to the possible vectors of human manipulation with this "open-weight" black box.
deckar01
The top of that leaderboard is filled with closed weight experimental models.
dvcky_db
This should surprise no one..Also Goodhart's law strikes again
labrador
Meta does themselves a disservice by having such a crappy public facing AI for people to try (meta.ai). I regularly use the web versions for GPT 4o, Deepseek, Grok, and Google Gemeni 2.5.
Meta is always the worst so I don't even bother anymore.
openplatypus
Meta doing something dodgy OR unethical OR criminal ... and nobody is surprised.
https://archive.is/Ec6V1