This research introduces fuzzy-function programming via the Program-as-Weights (PAW) paradigm. The method compiles natural-language specifications into compact, locally executable neural artifacts using a 4B compiler and a 0.6B interpreter. This approach matches the performance of much larger models while using a fraction of the memory and providing faster inference. By shifting the role of foundation models from per-input solvers to tool builders, PAW allows for the creation of small, reusable, and efficient programs for tasks that resist rule-based implementation.
Main points:
- Proposes Program-as-Weights (PAW) to compile natural language into neural programs.
- Uses a 4B compiler and 0.6B interpreter for efficient local execution.
- Achieves high performance with significantly reduced memory requirements.
- Reframes foundation models as tools for building reusable artifacts.
- Releases the FuzzyBench dataset with 10M examples.
Main points:
- Proposes Program-as-Weights (PAW) to compile natural language into neural programs.
- Uses a 4B compiler and 0.6B interpreter for efficient local execution.
- Achieves high performance with significantly reduced memory requirements.
- Reframes foundation models as tools for building reusable artifacts.
- Releases the FuzzyBench dataset with 10M examples.
