Bellman Efficiency after Abilities Have Been Merged

Authors

  • Justin Allen Author
  • Noah Lawson Author
  • Owen Palmer Author

Abstract

A fast robust Bellman update and cross-modality model merging change different stages of a decision system. Evaluation should preserve transition assumptions, ambiguity scale, and modality-specific outcomes. Efficient adaptation adds a resource option whose actual cost needs measurement. Solver speed or a favorable merged-model average cannot establish that the complete representation supports grounded robust action. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.

References

  1. S. Chen, J. Zhang, T. Zhu, W. Liu, S. Gao, M. Xiong, M. Li, and J. He, “Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging,” Proceedings of the 42nd International Conference on Machine Learning, 2025.
  2. Z. Yu, L. Dai, S. Xu, S. Gao, and C. Ho, “Fast Bellman Updates for Wasserstein Distributionally Robust MDPs,” Advances in Neural Information Processing Systems, pp. 30554–30578, 2023.
  3. Tong, H., Xu, G., & Chen, Y. (2026, May). Hadamard Tensor Ring for Efficient Low-Rank Fine-Tuning. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  4. Li, Yuanhao, Mingshan Liu, Hongbo Wang, Yiding Zhang, Yifei Ma, and Wei Tan. “DRAFT-RL: Multi-Agent Chain-of-Draft Reasoning for Reinforcement Learning-Enhanced LLMs.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 40, no. 35, 2026, pp. 29530-29537. https://doi.org/10.1609/aaai.v40i35.40195.
  5. Kumar, Aviral, Aurick Zhou, George Tucker, and Sergey Levine. “Conservative Q-Learning for Offline Reinforcement Learning.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 1179–1191. https://arxiv.org/abs/2006.04779.
  6. Lewis, Patrick, et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459–9474. https://arxiv.org/abs/2005.11401.
  7. Geifman, Yonatan, and Ran El-Yaniv. “Selective Classification for Deep Neural Networks.” Advances in Neural Information Processing Systems, vol. 30, 2017. https://arxiv.org/abs/1705.08500.
  8. Ovadia, Yaniv, et al. “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift.” Advances in Neural Information Processing Systems, vol. 32, 2019. https://arxiv.org/abs/1906.02530.
  9. Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.
  10. Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
  11. Hu, Edward J., et al. “LoRA: Low-Rank Adaptation of Large Language Models.” International Conference on Learning Representations, 2022. https://openreview.net/forum?id=nZeVKeeFYf9.
  12. Guo, Chuan, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, PMLR, vol. 70, 2017, pp. 1321–1330. https://proceedings.mlr.press/v70/guo17a.html.

Downloads

Published

2025-06-11

Versions

Issue

Vol. 1 No. 6 (2025): June 2025

Section

Articles