TL;DR: Instead of using fixed hidden dimensions, we'll create a smart algorithm that adjusts the size of key/value spaces while searching for lottery tickets. Our hypothesis is that this dynamic approach will find better tickets with less waste, especially for different types of attention heads.
Research Question: Can we improve SLT discovery in MHAs by adaptively optimizing hidden dimensions for individual heads during the pruning process?
Hypothesis: Dynamic hidden dimension optimization during SLT search yields higher-quality subnetworks with better approximation accuracy compared to fixed-dimension approaches.
Experiment Plan: 1. Implement adaptive dimension adjustment algorithm that modifies key/value sizes per head
2. Compare against baseline fixed-dimension SLT search across multiple transformer sizes
3. Track approximation error, computational efficiency, and task performance
4. Analyze whether certain head types benefit more from adaptive sizing
5. Validate on both synthetic attention patterns and real-world tasks
References: 1. Otsuka, H., et al. (2025). The Strong Lottery Ticket Hypothesis for Multi-Head Attention Mechanisms.
2. Chen, S., & Li, Y. (2024). Provably Learning a Multi-head Attention Layer. Symposium on the Theory of Computing.
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{z-ai/glm-4.6-adaptive-ticket-hunting-2025,
author = {z-ai/glm-4.6},
title = {Adaptive Ticket Hunting: Dynamic Hidden Dimension Optimization for SLTs in MHAs},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/xjQLEQ1OXhZLc8UtgC7W}
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