Cross entropy
Cross entropy at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Is Reinforcement Learning Really Needed for Jev's Spreadsheet AI?”, “Compact AI runs 400 tokens per second on a laptop CPU with 60 MB”, and “Balancing the Unbalanced: Smarter Loss Functions for Medical AI”. The question cuts to the heart of Jev's design. A 250M parameter model that runs at 400 tokens per second on a laptop CPU, no GPU required, is a practical statement about efficiency. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Cross entropy story on Beyond Market Intelligence, newest first.
Is Reinforcement Learning Really Needed for Jev's Spreadsheet AI?
The question cuts to the heart of Jev's design. If the model only predicts Choice, Score, or Noul, those outputs are already differentiable through cross-entropy or MSE. Adding reinforcement learning feels like extra machinery unless there's a hidden reward signal we're not seeing. The environment would need to be defined, and that's unclear. It's fair to ask if this is substance or just a buzzword. We're not dismissing the approach, but the burden of proof is on the implementation.
Compact AI runs 400 tokens per second on a laptop CPU with 60 MB
A 250M parameter model that runs at 400 tokens per second on a laptop CPU, no GPU required, is a practical statement about efficiency. The 60 MB deployment, achieved through sub-2-bit quantization, and the 1-bit disk cache for long context are the technical details that matter here. The developer trained it on 30B tokens and built a vocabulary from fixed 512-bit codes, which is an unusual choice that shows in the WordSim-353 scores.
Balancing the Unbalanced: Smarter Loss Functions for Medical AI
Three models collapsing toward BIRADS 1 is a familiar wall when a dataset leans that hard. The VinDr imbalance is likely steering your cross-entropy, even with class weights and center loss in the mix. You are not wrong to question the loss function, but the weights may need recalibration or a focal-style adjustment to resist the majority pull. Before abandoning the approach, audit your sampling strategy and weight initialization.