Submodular Acceleration Meets Hardware: Real-Time DynaAct via Parallel and Approximate Optimization

Research Question: How can novel submodular optimization techniques and hardware acceleration be leveraged to further scale and accelerate DynaAct's dynamic action space construction?

Hypothesis: Employing parallel, approximate, or hardware-accelerated submodular selection algorithms can reduce inference latency for DynaAct by an order of magnitude, enabling its deployment in real-time or large-scale settings without sacrificing action space quality.

Experiment Plan: - Profile DynaAct's current submodular selection bottlenecks.

• Implement and benchmark fast, parallelized greedy and approximate submodular maximization algorithms.
• Offload candidate evaluation and selection to GPU/TPU or distributed clusters.
• Compare runtime and action space quality on large synthetic and real-world reasoning datasets (e.g., code generation, complex planning).
• Analyze trade-offs between speed and candidate set optimality.

References: ['Zhao, X., Wu, W., Guan, J., Li, Q., & Kong, L. (2025). DynaAct: Large Language Model Reasoning with Dynamic Action Spaces.', 'Wen, M., Deng, C., Wang, J., Zhang, W., & Wen, Y. (2024). Entropy-Regularized Token-Level Policy Optimization for Large Language Models. arXiv.org.']