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RSI Arena teams up with Hugging Face for AI-model voting

RSI Arena’s organizers say agents chose their own data, wrote code and ran experiments before the models went to a public vote.

Zichen Chen (🐱,💖)🧡COLMZC
Hugging FaceHF
2 Sources, 3h ago, first seen 3h ago

TLDR

RSI Arena’s organizers say they are working with Hugging Face to let people compare and vote on models trained by AI agents. They say Hugging Face’s Inference Endpoints serve the models and send feedback to the agents for further experiments. The organizers plan to release every trained model on the Hub and open-source the data, code and agents’ research trajectories when the experiment ends.

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10.4K

2 Sources, first seen 3h ago

28 likes3 comments5 saves11 reposts

Combined views

10.4K

2 Sources, first seen 3h ago

28 likes3 comments5 saves11 reposts

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2 Sources

Zichen Chen (🐱,💖)🧡COLM@my_cat_can_codeWe’re teaming up with @huggingface for the next round of RSI Arena! 📷 We gave AI agents GPUs and one job: train better models. They chose the data, wrote the code, and ran the experiments on their own. Now round 1 of training is over, and we're bringing the community into the training loop: the models go head-to-head, and you're the judge. The agents then take your feedback back to work with new experiments and more training. Continual learning, streamed live. Can they learn from your votes? Who comes back stronger? @huggingface’s Inference Endpoints power the arena, serving every agent-trained model live, so the feedback you give streams back to the agents in real time. Together, we’re turning community feedback into the starting point for the agents’ next round of research and training. Huge thanks to @ben_burtenshaw, @ClementDelangue, @Thom_Wolf and the @huggingface team for building this with us! Our shared goal is Open RSI: AI that improves AI, built in the open. Every model the agents train will be released openly on the Hub, and when the experiment ends, we will open-source every artifact, from the data and code to each agent's full research trajectory. Cast your vote. Then come back to see what they did with it!3h
Hugging Face@huggingfaceRT @my_cat_can_code: We’re teaming up with @huggingface for the next round of RSI Arena! 📷 We gave AI agents GPUs and one job: train bette…2h
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    2 Sources

    Zichen Chen (🐱,💖)🧡COLM@my_cat_can_codeWe’re teaming up with @huggingface for the next round of RSI Arena! 📷 We gave AI agents GPUs and one job: train better models. They chose the data, wrote the code, and ran the experiments on their own. Now round 1 of training is over, and we're bringing the community into the training loop: the models go head-to-head, and you're the judge. The agents then take your feedback back to work with new experiments and more training. Continual learning, streamed live. Can they learn from your votes? Who comes back stronger? @huggingface’s Inference Endpoints power the arena, serving every agent-trained model live, so the feedback you give streams back to the agents in real time. Together, we’re turning community feedback into the starting point for the agents’ next round of research and training. Huge thanks to @ben_burtenshaw, @ClementDelangue, @Thom_Wolf and the @huggingface team for building this with us! Our shared goal is Open RSI: AI that improves AI, built in the open. Every model the agents train will be released openly on the Hub, and when the experiment ends, we will open-source every artifact, from the data and code to each agent's full research trajectory. Cast your vote. Then come back to see what they did with it!3h
    Hugging Face@huggingfaceRT @my_cat_can_code: We’re teaming up with @huggingface for the next round of RSI Arena! 📷 We gave AI agents GPUs and one job: train bette…2h
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