Jiayi Pan, a PhD candidate at UC Berkeley, has led a team to recreate key functionalities of DeepSeek's R1-Zero at a fraction of the cost—around $30. This signifies a shift in AI research towards more efficient solutions, challenging the costly models used by giants like OpenAI. The small language model, named 'TinyZero', incorporates reinforcement learning and can solve a number-based game. Currently available on GitHub, the team aims to produce a paper to further clarify its research aspirations, promoting accessibility in AI development.
We hope this project helps to demystify the emerging RL scaling research and make it more accessible.
The results: it just works!
Take it with a grain of salt until other experts weigh in and test it for themselves.
The assertion - and particularly its bargain basement price tag - is yet another illustration that the discourse in AI research is rapidly shifting.
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