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Modern large language models - transformers and diffusion language models - are built around two canonical algorithmic tasks: prediction and generation.
Test-time scaling improves LLM reasoning by generating and aggregating multiple candidate answers, yet many pipelines use fixed per-query budgets that spend the same compute on easy and difficult…
The commitments show that a substantial part of Big Tech's AI spending spree has already been locked in, without yet appearing as debt-like lease liabilities on company balance sheets.
Human input reaches language models by typing or speaking, and each channel leaves a distinct signature: orthographic noise for keyboards; for voice, disfluency from conventional transcription and…
On-policy training has emerged as a powerful post-training paradigm for improving the reasoning capabilities of large language models, and is often enhanced by golden trajectories from stronger expert…
Hank Green's AI apology has sparked a wider conversation, as creators discover that generative AI can be a risk to their reputation.
