# Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control

arXiv: https://arxiv.org/abs/2608.00908 · HTML: https://mlsanity.com/paper/2608.00908/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 58 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning](https://mlsanity.com/paper/2006.16908/) (2020)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [IPO: Interior-point Policy Optimization under Constraints](https://mlsanity.com/paper/1910.09615/) (2019)
- [Network Diffuser for Placing-Scheduling Service Function Chains with Inverse Demonstration](https://mlsanity.com/paper/2501.05673/) (2025)
- [Network Function Virtualization: State-of-the-art and Research Challenges](https://mlsanity.com/paper/1509.07675/) (2015)
- [An Approximate Dynamic Programming Algorithm for Monotone Value Functions](https://mlsanity.com/paper/1401.1590/) (2014)
- [Geometry of Drifting MDPs with Path-Integral Stability Certificates](https://mlsanity.com/paper/2601.21991/) (2026)
- [Network Slicing for 5G with SDN/NFV: Concepts, Architectures and Challenges](https://mlsanity.com/paper/1703.04676/) (2017)
- [Intent-driven autonomous network and service management in future cellular networks: A structured literature review](https://mlsanity.com/paper/2108.04560/) (2021)
- [LiSFC-Search: Lifelong Search for Network SFC Optimization under Non-stationary Drifts](https://mlsanity.com/paper/2602.14360/) (2026)
- [NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search](https://mlsanity.com/paper/2605.00751/) (2026)
- [Operator-Guided Invariance Learning for Continuous Reinforcement Learning](https://mlsanity.com/paper/2605.06500/) (2026)
- [Metric-Gradient Projection for Stable Multi-Agent Policy Learning](https://mlsanity.com/paper/2605.18809/) (2026)

## Built on by (most cited first, 2 of 2)

- [Geometry-Aware Operator Families for Structured Representation Learning](https://mlsanity.com/paper/2609.33089/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

Modern network policy control maps intent to sequential placement-control decisions. Bellman-style policy optimization primarily asks which action to optimize, while constraints are commonly handled through penalty, barrier, or Lagrangian mechanisms. We observe that before a value function can certify the best deployment, intermediate signals may already identify many candidates that should be excluded from further optimization. This motivates a complementary direction: Learning Not to Optimize. Before a value function is accurate enough to select the best placement-control decision, intermediate signals may already show that candidates are equivalent under state--intent relabeling (quotienting), lead to a uniformly worse future state (dominance), or violate executable network laws (residual screening). LNOQRD uses these computed or learned signals as a shadow process to reshape the domain on which primal policy optimization is performed, thereby reducing the action space. We prove lossless quotienting and dominance under explicit equivariance and monotonicity conditions, bound frontier size and ranking cost, and quantify losses from approximate certificates and primal estimates. Experiments show that LNOQRD reduces small-instance candidates by 75.9% while retaining 90.8% near-oracle coverage and, on large instances, achieves the highest utility and intent satisfaction, the lowest hard-law violation and post-generation latency, and a 73.0% average reduction among candidate-based baselines.