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Forgetting earlier observations, not decision-making flaws, is the primary source of errors in multimodal LLMs navigating complex tasks.
Diffusion language models can now match autoregressive quality, thanks to a clever trick that forces them to agree with themselves.
Forget textual rules and coarse embeddings: a multimodal reward model that directly compares rendered visuals unlocks significant gains in vision-to-code RL.
