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how is this open vs whatdeepseek did?
deepseek claims they are "open source" but they are not. They are open weight. IMO a truly "open" AI model should have 3 components publicly available: the weights, the code, and the dataset. Without all 3 the model is not reproducible. Could make the argument that the code and data are sufficient though.
how is this open vs whatdeepseek did?
From that article: > The release of DeepSeek-R1 is an amazing boon for the community, but they didn’t release everything—although the model weights are open, the datasets and code used to train the model are not. > The goal of Open-R1 is to build these last missing pieces so that the whole research and industry community can build similar or better models using these recipes and datasets.
Am I missing something?
What are some other domains outside of Math and Coding that would be suitable for RL with automated verification?
Exciting to see this being reproduced, loving the hyper-fast movement in open source! This is exactly why it is not “US vs China”, the battle is between heavily-capitalized Silicon Valley companies versus open source. Every believer in this tech owes DeepSeek some gratitude, but even they stand on shoulders of giants in the form of everyone else who pushed the frontier forward and chose to publish, rather than exploi…
Given DeepSeek's open philosophy I wonder what their response is to simply being asked for access to the code and data that this project intends to recreate?
Does anyone know how difficult it is to perform this kind of reproduction? E.g. how much time would it take (weeks? years?) and how likely it is to succeed?
Earlier quoted context omitted.
the timescales were larger
Much larger. Static HTML AJAX WebSocket, for example weren't it decades in between?
- HTML first released in 1993
- AJAX in 1999
- Websocket first proposed in 2008
https://en.wikipedia.org/wiki/HTML https://en.wikipedia.org/wiki/Ajax_(programming) https://en.wikipedia.org/wiki/WebSocket
Is this what the Web was like in the beginning? Something exciting and fascinating every week?
Every now and then we still experience the power of collaborative work fueled by open source and not driven by money but curiosity and collegiality. This is the thing I miss the most from the early internet years.
It's amazing how differently people interact with each other when collaborating on a passion project. For me, opensource software is the best way to do it. Pick a topic you're passionate about and start contributing somewhere :)
Exciting to see this being reproduced, loving the hyper-fast movement in open source! This is exactly why it is not “US vs China”, the battle is between heavily-capitalized Silicon Valley companies versus open source. Every believer in this tech owes DeepSeek some gratitude, but even they stand on shoulders of giants in the form of everyone else who pushed the frontier forward and chose to publish, rather than exploi…
Ah yes the "open source" code that was not released by the DeepSeek team and the tens of thousands of professional grade GPUs that were contributed by the "community".
DeepSeek is based on Llama which was produced by ... Meta.
Earlier quoted context omitted.
From that article: > The release of DeepSeek-R1 is an amazing boon for the community, but they didn’t release everything—although the model weights are open, the datasets and code used to train the model are not. > The goal of Open-R1 is to build these last missing pieces so that the whole research and industry community can build similar or better models using these recipes and datasets.
Genuine question, but how do you replicate the effort exactly without $5M in compute? and can you test that the published weights etc are actually those in the model? Am I missing something?
The R1 trick looks like it may be a whole lot cheaper than that. R1 apparently used just 800,000 samples - I don't fully understand the processing needed on top of those samples but I get the impression it's a whole lot less compute than the $5.5m used to train v3.
Earlier quoted context omitted.
I like that it’s open source, but ultimately it is china so we can’t trust it. It’s trivial to implement bias in models (hence the no-no filters in chatgpt) so if they’re smart they’ll do what they do with tiktok and make the answers different for their rivals.
The thing about open source is that you don't need to trust it. They shared their methodology, so if they are legit, someone else will reproduce what they did very quickly. I expect Meta, Amazon, Google, and Anthropic are on the case right now. From this list, the only one I trust any more than I trust Deepseek is Anthropic. The other three have shown they'll instantly bend the knee to whomever is in power, and that'…
Will you kindly explain why? Thank you.