Cmpnd releases dspy-wsp weight-space optimizer
MarkTechPost: Cmpnd Releases dspy-wsp: Weight-Space Programming Comes to DSPy
Cmpnd has released dspy-wsp, a prototype optimizer that brings weight-space programming to DSPy. Weight-space programming is a technique in which a frontier LLM reads another model’s weights and edits them directly. There are no gradients, no training loop and no fine-tuning dataset. dspy-wsp packages this as WeightSpace, an optimizer that plugs into any DSPy program and metric. In Cmpnd’s tests, about $5 to $10 of GPT-6-Astra calls improved every decision model they tried. You can
Weight-space programming is a technique where a frontier language model reads and directly edits another model's weights to improve its performance, without using gradients, training loops, or fine-tuning datasets. Cmpnd's dspy-wsp release packages this approach as an optimizer that integrates into DSPy programs, allowing targeted edits to model weights at a relatively low cost. This differs from traditional prompt optimization, which edits only text instructions, and fine-tuning, which requires training data and gradient descent. The approach shows promise for making cheap, verifiable improvements to smaller models, though it runs slowly and has shown weaker results on larger models.
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