Meta-Learning the Gates: Adaptive Control of KL, Geometry, and Precision in RLVR

Research Question: Can we meta-learn or adaptively tune the strength and configuration of the Three-Gate Theory components (KL, geometry, precision) to maximize RLVR’s efficiency and transferability across tasks?

Hypothesis: Dynamic, data-driven adjustment of gate parameters (e.g., KL constraint strength, subspace selection criteria, update precision) will lead to more robust and generalizable RLVR training than static heuristics.

Experiment Plan: - Implement meta-learning or reinforcement learning controllers that adjust gate parameters during RLVR training.

• Evaluate on a variety of reasoning tasks and architectures, measuring convergence speed, stability, and transferability.
• Compare with fixed-gate baselines.
• Look for improved data efficiency, transfer learning, or resistance to catastrophic forgetting.

References: ['Zhu, H., Zhang, Z., Huang, H., Su, D., Liu, Z., Zhao, J., Fedorov, I., Pirsiavash, H., Sha, Z., Lee, J., Pan, D. Z., Wang, Z., Tian, Y., & Tai, K. S. (2025). The Path Not Taken: RLVR Provably Learns Off the Principals.', 'Tang, X., Zhang, Z., Liu, Y., Zhao, W. X., Wen, Z., Zhang, Z., & Zhou, J. (2025). Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward. arXiv.org.']