Always glad to see more open-weight models, but this caption on the 2nd demo image had me do a double-take: "Land or Water Generalization Experiment: We recreated the viral X puzzle by asking Beam to create a fixed 180×90 grid for longitudes -179° to 179° and latitudes -89° to 89°, with 16,200 points. This puzzle is a few days old, so could not appear in the training data, thus testing the model’s generalization. Beam gets 95.5% coverage right, putting us between Opus 5 (92.5%) and Fable 5 (97.8%), which shows how well it generalizes to novel new tasks."<p>Oof, no, this "puzzle is a few days old" is incorrect even if it's a social media trend just recently. Asking a model to generate a world map in this way is _at least_ from August 2025 as it appeared on LessWrong at that time: <a href="https://www.lesswrong.com/posts/xwdRzJxyqFqgXTWbH/how-does-a-blind-model-see-the-earth" rel="nofollow">https://www.lesswrong.com/posts/xwdRzJxyqFqgXTWbH/how-does-a...</a>
Yeah I remember when the original post about this came out. Def not recent. Though I think their point survives in that they didn't exactly RL on this.
It could be old but still not be part of the training set.
I'd love to see these tests repeated for the current frontier models...
Seems rather sloppy to not validate it wasn’t in their training or RL data.
I don't think age of the puzzle even matters, all models have search capacities these days
> all models have search capacities these days<p>one would hope that they disable websearch and internet access (maybe all tools?) when doing generalization testing?
Model weights (what is being tested here) don't inherently "access the web" when inference is running. If the model has access to a web search tool, that's a different story.
Is search part of the model or the harness?
If they made that statement and knowingly had search enabled, it would essentially be fraudulent.
What kind of company or organization is Reflection?<p>I think it is ever more important to realize <i>who</i> is releasing models rather than what the models do and how they compare.<p>Because models iterate at breakneck speed, looking at today's benchmarks is only useful for someone <i>using</i> the models today. Whereas if one builds a product on top of it, or commits to one for a project or team, the company or organization behind it, is far more important. Will they exist in a few months? Do they need a business-model? Are they subsidizing usage with venture capital and how long can they keep this up?<p>Is reflection a company? University lab? NGO?
Looks like general reasoning is their target. As for who:<p>> The startup was launched in March 2024 by Misha Laskin, who led reward modeling for DeepMind’s Gemini project, and Ioannis Antonoglou, who co-created AlphaGo, the AI system that famously beat the world champion in the board game Go in 2016.<p>with the obligatory:<p>> Investors in Reflection AI’s latest round include Nvidia, Disruptive, DST, 1789, B Capital, Lightspeed, GIC, Eric Yuan, Eric Schmidt, Citi, Sequoia, CRV, and others.
<a href="https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be-americas-open-frontier-ai-lab-challenging-deepseek/" rel="nofollow">https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be...</a>
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> Beam is a sparse Mixture-of-Experts model with 501 billion total parameters, 23 billion active, built for coding, reasoning, and agentic workloads.<p>> Beam’s capabilities come from major investments in both pretraining and reinforcement learning (RL). We pretrained the model on 23.8 trillion diverse, curated, high-quality tokens from the web and proprietary licensed datasets, matching or outperforming available similar-sized open base models. In parallel, we developed the algorithms, training environments, and infrastructure needed to sustain high-compute RL at exceptional scale. Our high-compute RL run generated over 100 million rollouts on 10.5K NVIDIA GB300 GPUs over 4 weeks of training.<p>Early access, no weights no tech details, just a sign up here for info
I'm all for more open models, but talk is cheap and this is a rather pointless announcement without anything backing it up. Publish your weights and HF repo or shut up IMO.
Is that all that a company about to give away the product of 10,000 GPUs running for a month gets to be now? give it away without a single promotional post or shut up? I support open source as much as the person but this is pretty caustic.
I am also starting to take issue with "we've developed a new cutting edge model, and nobody can use it" announcements. Most recently with Google's Argon, at least they were using it internally and they'd be slowly rolling it out. This one is from a company I've never heard of, they're not releasing weights <i>or</i> offering API access at this point, it feels like a fairly worthless announcement.
Yes -- because they're late to the party (high performing open weights models have been a thing for a couple of years now), and therefore will be compared with all the other open-weights model providers they are competing against.<p>It's not just that they are doing users a favor with weights; they are just as much seeking favors with attention and usage (in a crowded market!).
> give it away without a single promotional post or shut up?<p>I think the point is that people are happy to see promotional posts <i>when they actually release it</i>, but only then and not before.<p>Unfortunately pinky promises from corporations to release something at some indeterminate time in the future aren't worth the bytes they're stored in, especially in the AI industry which is full of grifters and charlatans.
There have been previous "we <i>will</i> release the model" when the model release never comes in the past.<p>I think the comment you are replying to is unnecessarily hostile too though.
The open weight community really is an odd one. Millions of Dollars for pre and post training given away for free and most often with very permissive licenses that allow commercial use (be it US, EU or mostly Chinese)<p>Yet going by the comments on localLlaMA or HN, those companies are the devil :-D. Colour me surprised.
You know, the other night I trained a model on my secret stash of GPUs, that now outperforms Opus 5.5 on pelican benchmark and Jev on classification speed, while running on a potato.<p>Will release weights soon.
In the case of Facebook, no good thing they do will ever undo the evil things they've done, let alone the evil things they're still doing right now. How convenient for the devil that a single act of "charity" should make him immune from all criticism. By all means, praise whatever good Facebook does in the world, but don't kid yourself about what Facebook is.
You don't get claim to be open and then not <i>release your actual product</i>.
Show me the mone^H^H^HWEIGHTS
They claim that they will release the model as open weights later this month.<p>That means that they have the 31th of October as the deadline to make true their claims.<p>The fact that they give early access to some may mean that they want some beta testers before the public release.
And also a "proprietary data set" hahaha... Probably just means they don't want to show it, and it is data, that either they shouldn't have, or that there is nothing special about their training data and it is just meant to sound like there is some secret ingredient, while there is none.
Not sharing the data is pretty standard because 1) it tends to get the lawyers involved and 2) good data is critical for getting good results.<p>Imo you can get better results with great data and generic modeling techniques than with incredible modeling techniques and crappy data. Because if you have crappy data, you won’t even know if your model is good because your evals will also be bad.<p>This is why Anthropic is throwing a fit about the Chinese distillation “attacks”. Clean reasoning traces are gold.
This isn't true.<p>Companies pay <i>lots</i> of money for proprietary agentic trajectories which are used during RL. These are things like "Task: summarize stock levels for months end accounting" which then traces the task though using SAP to look at different SKU stock levels, exporting them and generating summary Excel spreadsheets.<p>This is very different to the "scrape the internet" datasets that a table stakes for training a LLM.<p>Xiaomi released a fairly developer-centric dataset like this here:
<a href="https://huggingface.co/datasets/XiaomiMiMo/MiMo-V2.6-RL-oss" rel="nofollow">https://huggingface.co/datasets/XiaomiMiMo/MiMo-V2.6-RL-oss</a><p>SpreadsheetRL is another fairly specialized dataset: <a href="https://spreadsheet-rl.github.io/" rel="nofollow">https://spreadsheet-rl.github.io/</a>
Data has copyright issues, so one can't share it generally without getting permissions from all of the copyright holders. The data is not theirs to share, anyways. The derived (learned) weights are a different matter.
True for the pre-training data scraped from diverse sources. Less so for the later stage data for RL which is by all account more of a differentiator. In most cases the labs themselves produced the data so they are the copyright holders (or they are borrowing it from other labs via distillation). A lot of it is synthetic data, and since you can't copyright AI output, it becomes less about copyright and more about trade secrets.
this is very normal for frontier lab companies. you need good data either synthetic or labelled (all the chinese open source models have their own armies of data labelers)
> Beam is undergoing final red-teaming and evaluations. You can sign up here for early access to the model.<p>> <i>We will release the weights, technical report, model card, and developer artifacts later this month</i>.
I thought it would be interesting to look at some key figures vs another contemporary model in the same weight class (DeepSeek V4.1 Flash)<p><pre><code> DS V4.1F Beam
LM total params 552B 501B
LM active params (prefill) 8B 23B
LM active params (decode) 16B 23B
N-gram/PLE params 196B 0
Pretrain tokens 45T 28T
Disk KV bytes/token (FP4) 890 No information
Vision Yes (pretrain) No
Weights available Yes (launch day) "This month"
Weights licence MIT Apache 2.0
</code></pre>
At first blush the benchmarks are impressive, but to paraphrase Linus: "Talk is cheap, show me the weights." :-)
Bigger and still worse than existing free Chinese models that are smaller? Open weight models are nice, but at this point it seems western models are very far behind Chinese ones, despite Chinese companies publishing a lot of their findings. I hope we get more open models and more providers, as being stuck with a model from China or US with no competition is risky.<p>Google does do a great job with Gemma models. It's one of the few language models actually good at language. OpenAI's top closed models can't even write norwegian correctly.
It takes time / few iterations to get it right (and it's moving target), but yes, expensive trial, my personal feeling is that they went a bit too high, at the same time who knows, maybe good move – as they're saying RL didn't plateau. It feels like they had something like $100M budget for it?
The world will be a better place when we stop espousing Chinese-anything. They don’t treat their people well, they don’t care about them, just cogs in a machine. I’m completely disgusted with how hn people offers China up on a platter like they’re an example of something to emulate, I wish I knew how we got to this point.
I hear this so much from westerners who have no connection to the people or the country.
-China passed a law forbidding companies from replacing workers with AI.
-China regularly punishes CEOs and corrupt government officials who do bad things.
China promotes open source to the world enabling everyone in every country to have equitable access.
From living in china for over 20 years and talking to many many people, the majority is happy with the trajectory of their country.
Could any for these be said of the west?
I won't get into specifics on which countries regularly throw bombs on children, use starvation and blockades as weapons, and completely disregard the massive dissatisfaction of their citizens...but it sure isn't china.
Please clean your own house first before you complain about your neighbor.
China didn't pass a law forbidding companies from replacing workers with AI. There was an unfair termination lawsuit that has been misconstrued as such <a href="http://english.scio.gov.cn/m/chinavoices/2026-04/30/content_118471189.html" rel="nofollow">http://english.scio.gov.cn/m/chinavoices/2026-04/30/content_...</a> but it was applying existing law. If they had followed the provisions of Article 41 (3) of the Labor Contract Law <a href="https://english.court.gov.cn/2015-08/17/c_761484_6.htm" rel="nofollow">https://english.court.gov.cn/2015-08/17/c_761484_6.htm</a> which explicitly authorizes layoffs if "The enterprise changes its line of production, introduces a major technological updating or adjusts its business method, and, after modification of the labor contracts, still needs to reduce its personnel," there would have been no problem.
When you live inside of a country that erases inconvenient news or information, of course it will feel like you have a curated experience. They can kill, silence and disappear people, but you will never be allowed to hear about it. They can grab a CEO that gets too cozy with western values and supporting democratic reforms, because Marxism-Leninism is in their constitution.<p>Of course in any society there will be people that steal money or other universally recognized crimes, so when the state wants to remain in power they will promote news about actions against bad guys while cleaning up any bad news about the government.<p>It produces masses of either oblivious people or people who know it's probably strange and wrong, but would rather accept it than bother with the hassle of trying to change anything when you know all past attempts have failed.<p>China carries out more executions than the entire world combined. Even when they caught US CIA spies, China executed them all.<p>If you look at the way all of these totalitarian or authoritarian countries aligned together seem to engage in diplomacy, they try to put forward an extremely normal face to cloak their country against negative perception.<p>Venezuela, Cuba, Russia, China, Iran. Torture, assassination, institutionalized rape as punishment, funding terrorism, mass killings, starvation, global harassment networks, intentionally killing over a million Americans from drugs alone as part of unrestricted warfare even subsidizing the precursors ONLY if they're sold outside of the country, ignoring international law and building fake islands in the sea, ramming the ships of neighboring countries, persecuting religious people, purging their military like Stalin and the list goes on.<p>They intentionally normalize disappearance so that when someone disappears in their society, it's not considered overly strange. They cannot contact their family, their lawyer, their friends or any other organizations. They will not experience a just process and acquittal is basically unheard of.<p>They do a bad job of caring about people or human life even if there are public statements to the contrary. They constantly fail their people and cause unnecessary death, then under-report it or censor it. They lied about their COVID deaths and even cleaned up any evidence so that no international investigation could occur and the CCP could frame it any way they like, they censor deaths caused by floods they themselves caused by their poor planning, they censor the fact that Chinese companies poorly constructed the buildings in Venezuela that collapsed in the earthquake killing 10s of thousands of people unnecessarily, they lie about the schools in China that collapsed killing countless thousands of students. They were thinking over half of all residential buildings in China would need to be replaced, it was that bad.<p>How can anyone living in a country like that, have a clear eyed view of how good their society is to live in, when any information that should help you better understand it so you can improve your own society, is simply vanished? Is it just like, well, so long as it doesn't happen to me, it's fine! I don't see the problem! That is insane.<p>The problem we have here in the west, is that the CCP was founded by Comintern which had open ambitions to achieve total worldwide communist revolution in order to erase capitalism since true communism cannot coexist with it and this was a major cause of World War 2. The CCP has regularly lamented the fall of the Soviet Union and aim to do everything possible to prevent the CCP from collapsing that way.<p>There isn't any obvious open communication from the CCP to that effect, but the way they are expanding globally, with unprecedented influence, infiltration, etc and the most rapid military buildout in history combined with all the countries aligned with China starting to attack their neighbors, the question is fair to ask. China has this philosophy of using your enemy's own sword against them, and it is thought that capitalist market reforms within China have basically been that. Iran has radicals inside its country that hold similar ambitions, behaviors and strategies for expanding Islamic revolution which is why they fund terrorist groups all over the region and pretend it's not actually them.<p>All these things and countless more, yet their diplomats try to flip the claims around to make it sound like actions by other countries are just as bad, when in fact they are not. You can compare things, you can make analogies, but the actual practice and the intentionality are entirely different. Whenever a western country makes some mistake, they milk it for all its worth as if it's normal, but they do it to try to establish the psychology that "in reality we are better than that, but if something did happen, look they did it too!"<p>I'm not saying that China wants their people to die from COVID, wants them to die in building collapses, wants them to be executed in secret prisons. There is evidence they would rather that not be the case, but it's more pragmatic than compassionate. So many of the problems would not have persisted for so long if people were able to communicate more openly about these things, but they think it would be a threat to the existence of the CCP.<p>The spectacle of China is partly an illusion and people being taught to think otherwise aren't a good source for identifying whether China is problematic.<p>That is all part of the conversation around Chinese AI models.<p>There are many great Chinese people, but the CCP is a threat to the whole world and so it's no longer optional to be vigilant about watching their behaviors. A thriving booming society post-industrialization is normal and simply more amplified with a large population like China, but that doesn't change the essential concerns. People can go live there and at the individual local scale maybe life seems fine, but in terms of world strategy and risks there is a bigger picture coming into focus.
This sentiment was really common circa 2005, or at least general anti-China rhetoric, where I lived in the Tri-state. Funnily enough, I have never felt more a cog, living here in the U.S., than I do right now. Maybe you are right, but my guess is wherever you live, yours is a bit of a glass house as well.
Yeah, we should only promote, like... Finnish, Norwegian, maybe Swiss products, like Apertus. Not this unethically-made baby oil[1] from repressive torment nexuses of USA or China.<p>[1] <a href="https://i.imgur.com/rJSG019.jpeg" rel="nofollow">https://i.imgur.com/rJSG019.jpeg</a>
I feel like this is a marketing miss. If they had held their announcement until the model was released, I would have grabbed it and started running it through my benchmarks. It probably doesn’t get a place in the rotation based on their own description of its performance, but now the weights live on the server, I’m probably following them on HF and I will remember to check in every time I ls the models folder. With the announcement only, none of that happens and I’m likely to forget about this by the time it actually gets released.<p>The email harvest move just doesn’t fit where we/they are in the cycle. There are established players and a buffet of models to choose from (plus a ton of empty hype). The first move at this point for any new entrant should be to show, not tell. Even an API only release with the promise to open weight would be better (actually probably all around better since most people can’t run this locally).<p>I wish this lab and all the labs releasing the best. It’s a brutal landscape to sink millions of dollars into for a guaranteed “behind x model from a year ago” evaluation. But, I do believe there is genuine innovation left to uncover.
What kind of machine does one need to run this model? What's the usual setup someone would have for running this?
This appears to be larger than DeepSeek v4.1 Flash, more expensive to run, and worse on every measured metric.<p>Am I missing something?
> Am I missing something?<p>It's pretty clear from their framing ("Beam advances the <i>Western</i> open-weight frontier") that one of their main selling points is not being a Chinese lab.<p>I can't imagine that mattering to many individuals, but I guess someone out there has a government contract that forbids the use of foreign models
Reflection raised on the idea of creating the "American Deepseek Project"
Who's funding this?
Looks like Nvidia, Eric Schmidt, Sequoia, and a host of others <a href="https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be-americas-open-frontier-ai-lab-challenging-deepseek/" rel="nofollow">https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be...</a>
I still don't get why, after over a decade on HN, people refuse to Google very simple questions.
Reciprocally, people probably do go find out. The great filter is also how many of them come back to post the useful interesting information.
I still don't get why, after 25 years of slashdot, still ask why people DRTFA and complain about it as meta commentary.<p>InB4: kids these days :shakes-fist-at-cloud:
Sometimes we just enjiy having a conversation with people. It's how we got many answers before Google exists. I believe such choices have many, positive effects on people that society is losing.
quesiton is more like "lets analyze the motivations behind funding this"
And a much better post would be "Just checked, and X, Y, and Z are funding this. My bet is that Y is funding it because $REASON..."<p>Asking an easily-searchable question is just lazy.
Profit. Always.
because my templeos goes straight from ring0 after bios straight into a ui for hackernews that only lets me scroll, click into comments and type comments.
I don't get why anyone posts anything on HN when they can just have an LLM generate an entirely self-contained conversation now.<p>/s<p>The conversation is why.
Multiple independent approaches are cool and all but fully open source model training (datasets, pipeline, checkpoints) should be taking advantage of being open and share runs/budget between different entities.
We're still at the stage where every new entrant is welcome in my opinion. Doesn't need to be record-breaking upon initial release.
It depends! If a startup is entering with a large model to face other larger models, it must be better at least in 1 meaningful dimension.<p>500B params performing worse than other OSS of the same size is pretty meaningless if no one will use it.
I disagree. Sure let them play and see if they can improve. But this model has more compute and more training data than the predecessors it fails to surpass. That only means their training regime is inferior if their predecessors did so much more with so much less. That inferiority should not be encouraged.
You don't just magically do better than everyone else on every metric on your first go at something. Doing worse than others and refining is how pretty much everything works.
The reality is they trained a model and it looks worse on benchmarks than Qwen or GLM. I don’t see how sharing the weights hurts anyone? Even when Llama 4 came out and it was a dumpster fire, it didn’t affect me personally.<p>> That only means their training regime is inferior if their predecessors did so much more with so much less<p>Hard to imagine how that wouldn’t be the case. They probably missed the boat on distilling Claude (or their lawyers said no), they probably didn’t hire an army of math PhDs to write reasoning traces, they don’t have millions of DAUs in a coding agent to train from, and they probably have less money, less experience, fewer top tier researchers, and fewer resources for experiments. They are an underdog without a doubt.<p>None of that means they shouldn’t release their model.
I wonder if that's an indication that they are not distilling which limits how good they can get.
Openai, grok, and Anthropic aren't distilling. Theyre just second class. It's not a big deal, we just shouldn't be lauding them for being second class.
> Openai, grok, and Anthropic aren't distilling<p>Says who? We know Grok does at the least. They admitted it openly.
Musk said under oath that they use distillation for Grok.
And it still sucks. My apologies to the Cursor team but that's just very very poor performance.<p>Alternative explanation is that the Chinese have far more technical talent than anyone else, along with the infra and capital to build out these models.<p>My money is on the latter explanation, tbh.
Reflection is explicitly marketed as the 'US' DeepSeek<p>seems like they are aiming to provide both inference and RLaaS for american companies and western govts. even if they never fully beat deepseek if they get close enough the fact that they're American will help them close deals
deepseek is from the evil east, this is from the virtuous west
Apparently there is more to making good models than copying everything on the internet.
12 yards long, 2 lanes wide,
65 tons of American Pride!
Canyonero! Canyonero!
Yes it's (hopefully) not distilled from every single major American provider.
new entrant in this weight class, US lab.
Beam goes brrrr
I remember being in the room with pretraining day 1 to help monitor the training job launch. Watching this model train from day 1 has been an amazing experience!
Is it worse than the top open-weight Chinese models? Yes, it is, but at least the West has joined the party, and hopefully they will iterate on this and keep up the pace. The Chinese labs will certainly release new and powerful versions soon, so it's all about relative pace right now.
Does this one also routes to Claude under the hood like Reflection 70B did? I recall they even run a basic regex to remove "Claude" from the output. Then they promised to be completely transparent on the postmortem (they claim they had no idea what had happened), but the postmortem never came
I don't find open-weight models that impressive anymore. MiMo-V2.6 already showed that you can have a not so crazy architecture and enough compute, the bottleneck is then just the data. OAI and Anthropic are largely the frontier models because of the synthetic data they made. They have a large customer base and have the user's data as well as knowing what tasks their customers use the models for and what domain they should get synthetic data for.
Interesting. Have heard about the need to create synthetic data for LLM's, but didn't realize it was such an important factor. Although, how big a factor synthetic data is the next question, but guess there is no way to truly verify how much difference it makes with these closed models.
This is literally what the labs have been doing over the past year. The whole idea of emergence is a lie, there is some interpolation and superhuman long evaluations the models can do, but almost all the gains are from synthetic data. They hire thousands of professionals and pay them as much as 200$/hour to create many tasks that they want the model to perform and use these to teach the model on how to do it with RL. OAI had 30,000 contractors from Mercor for Sol 5.6.
When you see Opus suddenly becoming great at blender or some other 3D graphics, that's because they hired professionals and had them do similar tasks that people are looking for. They keep having better professionals at each iteration and so the quality improves. There is no emergence or "General" intelligence. The model doesn't learn to become better at a task because of scaling laws or emergence or whatever they might wanna say, it is literally RL on tasks that they want the model to perform well on.
Also interesting. Do you happen to know if once they've used those thousands of contractors to teach the model something like Blender (or some similar app) using RL, when they training a new model, do they need to use same contractors again to teach that new model the same behaviors?<p>The reason I ask is even though it can take hundreds or thousands of contractors to teach a model a certain behavior, wonder if they really only need to do it once for each desired behavior? (of course, future models might expand and refine this previous training) Because if that is the case, then wow, then future models can really expand their capabilities really very fast.<p>...Am wondering if they somehow record a training session so they can play it back whenever they need to train a new model with the same info? Or maybe the new models can just use distillation from the old model to relearn the old behaviors?
The contractors don't directly teach the model. They create datasets. Mostly they create tasks within an environment that the current generation of models wouldn't be able to do, they then write maybe a solution, a set of rules for evaluating the response, and whatever is needed for the RL. These tasks form a dataset. You can see examples of a task in the Mimo dataset that was open-sourced recently. The dataset would then be used by engineers for post-training of whatever model. Some of the model iterations that are released every month tend to be just a further post-training of the same base model that was pre-trained months ago. OAI recently has been doing a lot more pre-training, but for a long time they had the same pre-trained base model. This is why you see so many releases done so fast by the labs, they just need to post-train the same base model on whatever task they think would be better suited, I would speculate based on what users want and what the benchmarks test for.<p>Regarding your second question, I think if they want to further post-train a model using new data, they wouldn't feel the need to re-train it again on the data that it has already being trained on. But you never know. If the model is a completely new pre-trained base model, then they could either train the model using all the data and/or use a previous model to teach it. There is definitely a bunch of tricks they do to evaluate the models and check the performance or whatever their recipe is. It's really up to what the engineers would prefer. But you get the core idea, the models are not suddenly coming up with how to use the Blender on their own, they are explicitly being trained on a dataset curated by a professional that teaches the model how to use Blender. Surely there is another aspect that if the model gets better at coding, then it also helps it become better at Blender, and you have that transfer learning. However, there is no emergence or a deity popping up. But you get people who were evaluating theses models on blender use and suddenly seeing the model ace their tasks and they think they are dealing with a super-intelligence. They then undergo an AI psychosis once they try and extrapolate the (super)-exponential improvement in that one task over the next few months and across all other domains.<p>Regarding your third question, I already answered at it. But, when it comes to training, they definitely freeze the weights after each run just in case an issue arises and they need to address it (a GPU not working or the loss value blowing up).
What you're talking about is not synthetic data. Synthetic data is data generated by an LLM. If the data is generated by a human, it's not synthetic.<p>You're talking about real data created and curated by humans to help in training LLMs.
Releasing open-weight models at this scale is a massive milestone for the community. Access to transparent model internals is foundational for trustworthy systems.
Beggar choosing: my kingdom for more 90B-133B MoE local models. Especially with disk offload, that is a function/performance sweet spot for Mac workstations with 64GB-128GB of RAM.
Hey since it's an open-weight model, I would love to know: how much model safety alignment have you done? Did you do any sort of post-training and what restrictions are in place<p>Ideally i would like to place my own restrictions and align from scratch, currently I am resolved to do harness alignment using tools like Prismor but would love to do my own post training alignment
> Where frontier open models like Kimi K3 remain ahead on raw capability, Beam's advantage is efficiency at inference time.<p>It's great to see a company that acknowledges it still needs improvement instead of making false claims.
I am really curious why after Qwen-3.8-flash and deepseek-v4.1-flash newer open weight models (or proprietary but they don't tell) don't use n-grams. It looks (to me) like they are a cheep way to add more knowledge to the model.<p>What do I mean by cheap? You can rely on the SSD to retrieve the relevant tokens as no computation is needed meaning you can leverage storage (or cpu ram if you don't have unified memory) to serve part of the model which (to my understanding) is much cheaper to get than GPU RAM.<p>Anyone know what am I missing? Or is it that the pace of iteration for labs slow enough that they can't actually leverage it yet?
DeepSeek Engram paper published: 12th January<p>Qwen3.8 Flash Next release date: 26th August<p>DeepSeek V4.1 Flash release date: 10th September<p>Current date: 6th October<p>I think they'll become more popular in the coming months. Also Gemma 4 PLE (April) is similar to DeepSeek Engram in a lot of ways, just with 1-grams.<p>On the proprietary model point: I'm personally curious about whether heavy n-gram offload is one reason Anthropic keep driving down their token vocabulary size (the other reason being eliminating the LM head gradient bottleneck).
Instead of yet another mediocre but fully-made-in-the-West open model (alongside Mistral, Trinity, Poolside, Inkling, etc etc) I'd really love for a Western neloab start the same way Qwen did: by focusing on post-training. Qwen's first release was a Llama 1 finetune [1]! Once they made it useful, they started working their way back in the stack to also do their own pretraining, etc. Starting with pretraining feels like such a waste: there's millions of dollars of crystallized compute and data sitting around in the Chinese model weights. Why not start with one of those, and only work your way back to pretraining once you've released something you can prove is useful?<p>1: <a href="https://en.wikipedia.org/wiki/Qwen" rel="nofollow">https://en.wikipedia.org/wiki/Qwen</a>
I'm on the waiting list... Couldn't find any download option, so I suppose it is only obtainable through their API. Strange way to distribute open weights model.
the performance chart puts the better open source models behind the fold making it seem like it outperforms them... but it doesn't! all for open source models but this announcement is misleading
I'm a big fan of open-weight models.<p>It's true that no benchmark communicates the whole picture, and we won't really know how it behaves until weights are out, but the performance here doesn't seem particularly groundbreaking just based on the benchmark.
The meaningful test begins after the weights arrive: fixed harness, network disabled, repeated trials, and real latency, memory, energy and cost per completed task.
Any time a new lab shows up, folks complain about how their models are worse. Really? It would be nice if a new comer comes from no where and beats everyone, but that's rarely the case. The good thing is that other labs/people are figuring out how to build this, and if they keep at it then this is as bad as it gets for them and it would hopefully get better. A new entrant to the market is good for everyone.
Reflection isn't really "from no where", they have huge financial backing, 10K of the latest GPUs, and many ex leads from the established labs.
honest question: how honest do you think people are about their improvements and performance compared to objective results when all you do is praise them?<p>participation awards are not helpful.
Someone should name their next model "Workhorse" just for SEO reasons.<p>It's interesting how the industry converged to this very term, given that very less work is being done by horses since quite a while.
I guess future AI agents might market things as a "workman" for the same reason, despite less work being done by people :)
Thankfully there wasn't mistagging, we could have ended with workjackass.
If Beam "rivals GLM 5.2 on reasoning" does it mean it's as good as GLM 5.3 Flash? (a much smaller model)
Very little in terms of the layers they use. Calling it now, they are using global layers everywhere, making the model basically unusable due to high KV Cache use.
Open-weight at 501B is huge. What was the eval setup - was web search disabled to test generalization?
Minimum hardware to run this model?? doable in consumer hardware?
Nitpicking but I really wish this benchmarks table were easier to read. Should show which columns win in each row and should not require horizontal scrolling to see across.
That was exciting... Will come back later when the weights are out and gguf'd.<p>Access is currently limited. We'll contact you if early access becomes available.
ok so they are comparing themselves to and claim to be beating GLM's last generation GLM 5.2 model, GLM 5.3 Flash is a monster, this is honestly embarassing
Noticed some good questions made dead at the bottom of this thread. Odd...
Open model that is not yet open or widely accessible via API. Primarily comparing to non-SOTA models like Inkling and GLM 5.2. Included comparison to GLM 5.3 and DeepSeek V4.1 Flash in the table, but not in the charts (I assume they would make them look bad). Also no results from AA Index or Arena.
very curious to see more about what kinds of hardware you can run this on and the perf. characteristics… on the face of it, it seems like optimizing for inference speed might(?) be good for running on smaller hardware, but i suppose it could be the other way around and it is actually much resource-hungrier for the number of parameters, etc. …
Congrats to the reflection team
Am I the only one who thought of the BEAM VM after the first word of thee title?
If you don't buy into "America good, China bad" narrative, this new entrant & release by Inclusion Ai is a lot more exciting by every measurable metric.<p><a href="https://github.com/inclusionAI/Ling" rel="nofollow">https://github.com/inclusionAI/Ling</a>
There are many measurable metrics and I don't see any that seem impressive. Care to share the ones you found exciting?
That seems to be from ~1y ago. What am I missing?
I don’t buy into that narrative, but I don’t understand why Ling’s release is more exciting?
This is like an ad for how great Deepseek V4.1 Flash is.
I am a great fan of open weights model, but off late I am starting to loose track on the capabilities of the latest models released and now a days most of the open weights models are above 100 B params which is not going to run in our laptops. What happened to Jev hype? A 501B Beam model is not going to help with it (Non AR Schema driven responding under 1 second).<p>I am more interested with a SOTA Frontier 8B-10B model. Is this even possible?
fauxpen like OpenAI? says open but no weights. Feels like a bid to get a buyout before the weights go public. Doesn't even have technical details.
Suppose I inherited a data center spanning several hundred acres full of GPUs and free electricity.<p>Where do I get the data?<p>I mean, this many models. They have to start somewhere.
I guess public datasets on HuggingFace and some shadow libraries content is enough to start.<p>e.g. fineweb dataset is 50TB <a href="https://huggingface.co/datasets/HuggingFaceFW/fineweb" rel="nofollow">https://huggingface.co/datasets/HuggingFaceFW/fineweb</a>
There are a lot of open-research on pre-training, post-training and RL data mixtures and sourcing.<p>I recommend checking papers from Datalogy, Nvidia Nemotron, Ai2 (Ollmo, Tulu, ...) and the recent model from Aleph Alpha if you want to learn more.
I heard you should ask Claude about this. Preferably with thousands of accounts, routed through residential proxies
If you ask a model, they will generally tell you where to get data. Modern frontier models have the large advantage of having tens if not hundreds of millions of users providing use cases to train against to improve their responses.
<a href="https://scale.com/data-engine" rel="nofollow">https://scale.com/data-engine</a> - you just buy it.
You can also hire teams to create data for you for higher quality.
Get data from Claude. That's what the Chinese (allegedly) do.
forget the data....sell it and go live your life!
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pretty wild that, per public sources, Reflection AI raised over $4 billion and hit a $25 billion valuation while operating in total stealth, without ever releasing a single public product until now. Beam (501B) seems be their first-ever model drop. Or am I missing something?