> 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
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.
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.
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)
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.
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.
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)
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.
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...
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.
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!
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.
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
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. …
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.
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?
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.
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.
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
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.
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
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.
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.
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.
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.
htrp | 2 hours ago
> 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
zelphirkalt | 2 hours ago
janalsncm | an hour ago
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.
vanuatu | 52 minutes ago
mlmonkey | 26 minutes ago
wronglebowski | 2 hours ago
Loquebantur | an hour ago
> We will release the weights, technical report, model card, and developer artifacts later this month.
wg0 | 2 hours ago
Where do I get the data?
I mean, this many models. They have to start somewhere.
Hamuko | 2 hours ago
kbwal7 | an hour ago
altcognito | 2 hours ago
petu | 2 hours ago
e.g. fineweb dataset is 50TB https://huggingface.co/datasets/HuggingFaceFW/fineweb
lucrbvi | 2 hours ago
I recommend checking papers from Datalogy, Nvidia Nemotron, Ai2 (Ollmo, Tulu, ...) and the recent model from Aleph Alpha if you want to learn more.
konfusinomicon | an hour ago
ttul | an hour ago
Ariarule | 2 hours ago
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...
extr | 2 hours ago
charlieyu1 | an hour ago
criemen | 56 minutes ago
one would hope that they disable websearch and internet access (maybe all tools?) when doing generalization testing?
dexwiz | 54 minutes ago
sharktheone | 2 hours ago
pstuart | 2 hours ago
drubs | 2 hours ago
jeremyjh | 56 minutes ago
drubs | 36 minutes ago
aeetes | 2 hours ago
brumbelow | an hour ago
keeganpoppen | 2 hours ago
NorwegianDude | 2 hours ago
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.
mirekrusin | an hour ago
onlyrealcuzzo | 2 hours ago
Am I missing something?
martini333 | an hour ago
dotancohen | an hour ago
halJordan | an hour ago
janalsncm | 53 minutes ago
> 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.
flockonus | 40 minutes ago
500B params performing worse than other OSS of the same size is pretty meaningless if no one will use it.
jstummbillig | an hour ago
Centigonal | an hour ago
swiftcoder | an hour ago
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
htrp | an hour ago
mirekrusin | an hour ago
atlasunshrugged | an hour ago
ipsum2 | 32 minutes ago
mirekrusin | an hour ago
aizk | 51 minutes ago
vanuatu | 50 minutes ago
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
zopper | an hour ago
hypfer | an hour ago
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.
hnedeotes | 33 minutes ago
michaelkdev | 55 minutes ago
eaf7e281 | 31 minutes ago
It's great to see a company that acknowledges it still needs improvement instead of making false claims.
segmondy | 22 minutes ago
TheArcane | 21 minutes ago
https://github.com/inclusionAI/Ling
vcryan | 21 minutes ago