Checkpoints for the paper: "One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment".
AI & ML interests
We focus on Natural Language Processing and Multimodal Learning, exploring generative AI across different modalities.
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Papers
One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment
AR-Omni: A Unified Autoregressive Model for Any-to-Any Generation
Checkpoints and data for the paper AR-Omni: A Unified Autoregressive Model for Any-to-Any Generation
checkpoints for the paper Parallel Test-Time Scaling for Latent Reasoning Models.
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ModalityDance/latent-tts-rm
Token Classification ā¢ 0.1B ā¢ Updated ā¢ 13 -
ModalityDance/latent-tts-colar
Text Generation ā¢ 1B ā¢ Updated ā¢ 21 -
ModalityDance/latent-tts-coconut
Text Generation ā¢ 0.1B ā¢ Updated ā¢ 20 -
ModalityDance/latent-tts-codi
Text Generation ā¢ 0.1B ā¢ Updated ā¢ 17
Checkpoints for the paper: "One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment".
checkpoints for the paper Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space
Checkpoints and data for the paper AR-Omni: A Unified Autoregressive Model for Any-to-Any Generation
Checkpoints and data for the paper Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning.
checkpoints for the paper Parallel Test-Time Scaling for Latent Reasoning Models.
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ModalityDance/latent-tts-rm
Token Classification ā¢ 0.1B ā¢ Updated ā¢ 13 -
ModalityDance/latent-tts-colar
Text Generation ā¢ 1B ā¢ Updated ā¢ 21 -
ModalityDance/latent-tts-coconut
Text Generation ā¢ 0.1B ā¢ Updated ā¢ 20 -
ModalityDance/latent-tts-codi
Text Generation ā¢ 0.1B ā¢ Updated ā¢ 17