https://x.com/_lewtun/status/1883142636820676965
https://github.com/huggingface/open-r1
Hugging Face Journal Club - DeepSeek R1 https://www.youtube.com/watch?v=1xDVbu-WaFo
Let someone else burn up their server farm to get initial model.
Then you can load it and take it from there
I'm hoping someone will make a distillation of llama8b like they released, but with reinforcement learning included as well. The full DeepSeek model includes reinforcement learning and supervised fine-tuning but the distilled model only feature the latter. The developers said they would leave adding reinforcement learning as an exercise for others. Because their main point was that supervised fine-tuning is a viable method for a reasoning model. But with RL it could be even better.
Here's what it says once decoded :
> The Queanamen Galadrid is a simple secret that cannot be discovered by anyone. It is a secret that is not allowed to be discovered by anyone. It is a secret that is not allowed to be discovered by anyone. It is a secret that is not allowed to be discovered by anyone. It is a se...... (it keeps repeating it)
consensus seems to be that the api is uncensored but the webapp is.
> Accuracy rewards: The accuracy reward model evaluates whether the response is correct. For example, in the case of math problems with deterministic results, the model is required to provide the final answer in a specified format (e.g., within a box), enabling reliable rule-based verification of correctness. Similarly, for LeetCode problems, a compiler can be used to generate feedback based on predefined test cases.
> Format rewards: In addition to the accuracy reward model, we employ a format reward model that enforces the model to put its thinking process between ‘<think>’ and ‘</think>’ tags.
This is a post-training step to align an existing pretrained LLM. The state space is the set of all possible contexts, and the action space is the set of tokens in the vocabulary. The training data is a set of math/programming questions with unambiguous and easily verifiable right and wrong answers. RL is used to tweak the model's output logits to pick tokens that are likely to lead to a correctly formatted right answer.
(Not an expert, this is my understanding from reading the paper.)
- i consider the deepseek v3 paper required preread https://github.com/deepseek-ai/DeepSeek-V3
- R1 + Sonnet > R1 or O1 or R1+R1 or O1+Sonnet or any other combo https://aider.chat/2025/01/24/r1-sonnet.html
- independent repros: 1) https://hkust-nlp.notion.site/simplerl-reason 2) https://buttondown.com/ainews/archive/ainews-tinyzero-reprod... 3) https://x.com/ClementDelangue/status/1883154611348910181
- R1 distillations are going to hit us every few days - because it's ridiculously easy (<$400, <48hrs) to improve any base model with these chains of thought eg with Sky-T1 recipe (writeup https://buttondown.com/ainews/archive/ainews-bespoke-stratos... , 23min interview w team https://www.youtube.com/watch?v=jrf76uNs77k)
i probably have more resources but dont want to spam - seek out the latent space discord if you want the full stream i pulled these notes from