yesterday at 7:30 PM
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."
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: https://www.lesswrong.com/posts/xwdRzJxyqFqgXTWbH/how-does-a...
yesterday at 7:48 PM
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.
yesterday at 10:13 PM
I'd love to see these tests repeated for the current frontier models...
yesterday at 8:49 PM
I don't think age of the puzzle even matters, all models have search capacities these days
yesterday at 8:54 PM
> all models have search capacities these days
one would hope that they disable websearch and internet access (maybe all tools?) when doing generalization testing?
yesterday at 10:59 PM
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.
yesterday at 8:56 PM
Is search part of the model or the harness?
yesterday at 9:56 PM
Maybe that's a rhetorical question but just in case - the search would always be part of the harness. A model is only handling next-token prediction for a given input. That token may be something like [[web search]] to invoke a tool call but the actual call would be handled by the harness.
yesterday at 7:19 PM
> Beam is a sparse Mixture-of-Experts model with 501 billion total parameters, 23 billion active, built for coding, reasoning, and agentic workloads.
> 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.
Early access, no weights no tech details, just a sign up here for info
yesterday at 7:41 PM
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.
yesterday at 7:39 PM
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.
yesterday at 8:48 PM
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.
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.
This is why Anthropic is throwing a fit about the Chinese distillation “attacks”. Clean reasoning traces are gold.
yesterday at 9:23 PM
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.
yesterday at 8:58 PM
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)
yesterday at 8:45 PM
> Beam is undergoing final red-teaming and evaluations. You can sign up here for early access to the model.
> We will release the weights, technical report, model card, and developer artifacts later this month.
yesterday at 10:28 PM
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)
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
yesterday at 10:29 PM
yeah agreed, let's normalize "show me the weights" in this era
yesterday at 7:48 PM
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.
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.
yesterday at 8:37 PM
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?
yesterday at 7:49 PM
This appears to be larger than DeepSeek v4.1 Flash, more expensive to run, and worse on every measured metric.
Am I missing something?
yesterday at 8:25 PM
> Am I missing something?
It's pretty clear from their framing ("Beam advances the Western open-weight frontier") that one of their main selling points is not being a Chinese lab.
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
yesterday at 8:27 PM
Reflection raised on the idea of creating the "American Deepseek Project"
yesterday at 8:31 PM
Who's funding this?
yesterday at 8:43 PM
Looks like Nvidia, Eric Schmidt, Sequoia, and a host of others https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be...
yesterday at 9:18 PM
I still don't get why, after over a decade on HN, people refuse to Google very simple questions.
yesterday at 10:03 PM
Reciprocally, people probably do go find out. The great filter is also how many of them come back to post the useful interesting information.
yesterday at 9:51 PM
quesiton is more like "lets analyze the motivations behind funding this"
yesterday at 11:38 PM
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..."
Asking an easily-searchable question is just lazy.
yesterday at 11:42 PM
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.
yesterday at 11:55 PM
I don't get why anyone posts anything on HN when they can just have an LLM generate an entirely self-contained conversation now.
/s
The conversation is why.
yesterday at 8:30 PM
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.
yesterday at 8:08 PM
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.
yesterday at 8:36 PM
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.
yesterday at 11:39 PM
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.
yesterday at 8:56 PM
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.
> That only means their training regime is inferior if their predecessors did so much more with so much less
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.
None of that means they shouldn’t release their model.
yesterday at 10:24 PM
I wonder if that's an indication that they are not distilling which limits how good they can get.
yesterday at 9:09 PM
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.
500B params performing worse than other OSS of the same size is pretty meaningless if no one will use it.
yesterday at 8:59 PM
Reflection is explicitly marketed as the 'US' DeepSeek
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
yesterday at 8:09 PM
Apparently there is more to making good models than copying everything on the internet.
yesterday at 8:59 PM
Yes it's (hopefully) not distilled from every single major American provider.
yesterday at 8:23 PM
new entrant in this weight class, US lab.
yesterday at 8:00 PM
Beam goes brrrr
yesterday at 7:41 PM
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!
yesterday at 8:54 PM
What sort of outputs or telemetry is monitored on a large pre-training run?
yesterday at 9:14 PM
Outside of ML metrics, you're monitoring the health of every piece of hardware in the system. You need to make sure that you have every GPU, every CPU, the PCIe buses, the networking fabric are all working without any errors. You need to ensure that you can respond as fast as possible to any possible error. One bad component can bottleneck the entire job.
yesterday at 11:51 PM
https://github.com/facebookresearch/metaseq/blob/main/projec...
I really enjoyed reading the log book from the training of OPT-175B at Meta… I guess it’s all classified info but it’d be fun to read a blog post about the crazy day to day issues you run into when doing things at this scale :)
yesterday at 11:40 PM
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.
yesterday at 8:54 PM
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.
yesterday at 9:19 PM
> Where frontier open models like Kimi K3 remain ahead on raw capability, Beam's advantage is efficiency at inference time.
It's great to see a company that acknowledges it still needs improvement instead of making false claims.
yesterday at 11:03 PM
That was exciting... Will come back later when the weights are out and gguf'd.
Access is currently limited. We'll contact you if early access becomes available.
yesterday at 10:56 PM
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.
yesterday at 7:48 PM
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
yesterday at 7:55 PM
Yes. All the link made me realize is that I should checkout Deepseek 4.1 flash
yesterday at 8:24 PM
Someone should name their next model "Workhorse" just for SEO reasons.
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.
yesterday at 9:16 PM
Thankfully there wasn't mistagging, we could have ended with workjackass.
yesterday at 7:59 PM
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.
yesterday at 9:28 PM
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.
yesterday at 11:44 PM
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?
participation awards are not helpful.
yesterday at 7:48 PM
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. …
yesterday at 7:34 PM
Am I the only one who thought of the BEAM VM after the first word of thee title?
yesterday at 7:53 PM
yesterday at 7:47 PM
nopes
yesterday at 8:16 PM
yesterday at 8:25 PM
yesterday at 7:35 PM
yesterday at 9:29 PM
This is like an ad for how great Deepseek V4.1 Flash is.
yesterday at 7:29 PM
Suppose I inherited a data center spanning several hundred acres full of GPUs and free electricity.
Where do I get the data?
I mean, this many models. They have to start somewhere.
yesterday at 7:43 PM
I guess public datasets on HuggingFace and some shadow libraries content is enough to start.
e.g. fineweb dataset is 50TB https://huggingface.co/datasets/HuggingFaceFW/fineweb
yesterday at 10:49 PM
I heard you should ask Claude about this. Preferably with thousands of accounts, routed through residential proxies
yesterday at 11:39 PM
You can also hire teams to create data for you for higher quality.
yesterday at 7:47 PM
There are a lot of open-research on pre-training, post-training and RL data mixtures and sourcing.
I recommend checking papers from Datalogy, Nvidia Nemotron, Ai2 (Ollmo, Tulu, ...) and the recent model from Aleph Alpha if you want to learn more.
yesterday at 7:34 PM
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.
yesterday at 7:30 PM
Get data from Claude. That's what the Chinese (allegedly) do.
yesterday at 8:10 PM
Note that this sort of distillation is NOT for pre-training data (which is tens of trillions of tokens). I think the allegations against Chinese companies by Anthropic is more so that they distill SFT data (which is good for post-training, but you still need a strong base model)
yesterday at 8:09 PM
forget the data....sell it and go live your life!
yesterday at 9:42 PM
[flagged]
yesterday at 9:58 PM
[flagged]
yesterday at 7:24 PM
[dead]
yesterday at 9:29 PM
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.
https://github.com/inclusionAI/Ling
yesterday at 10:28 PM
That seems to be from ~1y ago. What am I missing?