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VLM-AR3L outperforms traditional reward learning methods by integrating absolute and relative rewards, enabling agents to navigate complex tasks with greater efficacy.
Masked diffusion language models can now achieve 21.8x better compute efficiency than autoregressive models, thanks to binary encoding and index shuffling.
