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Apple Transformer 潛在預瞄訓練

Apple Transformer 潛在預瞄訓練

Apple 的 Latent Lookahead Training for Transformers 論文獲 ICLR 2026 潛在與隱式思考工作坊接受。該方法解決自迴歸模型的限制,讓模型能在不早早承諾的情況下探索多種延續可能。透過非均勻運算分配,提升模型在困難 token 上的表現力。

Apple Machine LearningOfficialMar 25#lookahead-training#autoregressive#latent-space
Cadmus: Low-Cost Program Synthesis System

Cadmus: Low-Cost Program Synthesis System

Apple ML introduces Cadmus, a small-scale system for autoregressive program synthesis. It features an integer virtual machine, a dataset of diverse true programs, and a transformer model trained for under $200 compute. This setup enables controlled experiments bypassing issues with large LLMs like OOD challenges and high resource demands.

Apple Machine LearningOfficialFeb 13#research#apple-ml#cadmus
Cadmus Enables Cheap Program Synthesis Experiments

Cadmus Enables Cheap Program Synthesis Experiments

Apple Machine Learning introduces Cadmus, a small-scale system for autoregressive program synthesis. It features an integer virtual machine, a dataset of diverse true programs, and a transformer model trained for under $200 compute. This setup allows controlled experimentation without the complexities of large LLMs.

Apple Machine LearningOfficialFeb 13#research#apple#cadmus
Cadmus: Affordable Autoregressive Program Synthesis

Cadmus: Affordable Autoregressive Program Synthesis

Apple ML introduces Cadmus, a small-scale system for autoregressive program synthesis. It features an integer virtual machine, a dataset of diverse true programs, and a transformer model trained for under $200 compute. This setup enables controlled experiments avoiding LLM pitfalls like OOD issues and high compute demands.

Apple Machine LearningOfficialFeb 13#research#apple-ml#cadmus