Cmpnd Releases dspy-wsp: Weight-Space Programming Comes to DSPy

Cmpnd released dspy-wsp, an MIT-licensed prototype DSPy optimizer called WeightSpace in which a frontier LLM (recommended gpt-6-astra) reads and edits a student model's weights directly, without gradients, a training loop or a fine-tuning dataset. In tests on open decision models from 0.6B to 9B parameters, runs of 25 writer passes cost $5.42 to $7.63 and took 78 to 167 minutes, with d1-omni-600M rising from 50.9% to 69.1% using WeightSpace plus ReAnchor, while the 9B Clef-Flash gained only +1.9 from WeightSpace alone. Cmpnd describes the package as very experimental and slow, and notes that under the original paper's stricter rule d1-omni-600M gained just +2.8 points.