Weight-Space Programming enables localized neural edits with up to 29.45-point gains
Original title: Exploring Weight-Space Programming: Capabilities, Generalization, and Limitations
16 trajectories on Qwen models reveal WSP's strong localized improvements but inconsistent generalization and reliability
Weight-Space Programming allows agents to edit neural parameters directly. The approach yields accuracy improvements of up to 29.45 points on MATH-500 and 36.72 points on IFEval by altering only 1,023 parameters. Cross-task generalization is promising but uneven, with six of 16 trajectories failing to validate improvements and retaining original weights.
- 1,023 parameters modified produce 29.45-point MATH-500 gain
- Math-edited checkpoint boosts GPQA Diamond by 3.03 points
- Six trajectories retain original weights
- Improvements observed on seven benchmarks across two model scales
From the original article
1 min read at huggingface.co
Weight-Space Programming (WSP) enables reasoning agents to directly inspect and modify neural network parameters, offering an alternative to gradient-based model adaptation. However, its capabilities, cross-task generalization, and practical limitations remain insufficiently understood. We systematically investigate WSP through 16 editing trajectories across seven benchmarks and two model scales (Qwen3.
By Junru Hu, Cheng Tang, Wei Li · huggingface.co
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