mlsanityData to 14 Oct 2026

Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models

arXiv 2605.11102 ↗First posted 11 May 2026cs.LG cs.AI cs.SYcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Neural warm starts can sharply reduce the number of Newton-Raphson iterations required to solve the AC power flow problem, but existing supervised approaches generalize poorly on heavily loaded instances near voltage collapse. We prove a lower bound on the Newton-Raphson iteration count that depends on the direction of the warm start error rather than on its magnitude, and show as a corollary that the bound becomes vacuous as the smallest singular value of the power-flow Jacobian shrinks, identifying the failure mode of supervised regression near the saddle-node bifurcation. Motivated by this analysis, we introduce Newton's Lantern, a finetuning pipeline that combines group relative policy optimization with a learned reward model trained on perturbations of the base model's predictions, using the iteration count itself as the supervisory signal. Across IEEE 118-bus, GOC 500-bus, and GOC 2000-bus benchmarks, Newton's Lantern is the only method that converges on every test snapshot while attaining the smallest mean iteration count.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 28 references that are in the map. First posted on arXiv 11 May 2026.

Builds on

8 of 28 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2024CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbationscited by 27
  4. 2025PFDelta: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variationscited by 7
  5. 2022Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flowcited by 4
  6. 2020Resilience Analysis and Cascading FailureModeling of Power Systems under Extreme Temperaturescited by 2
  7. 2025Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution gridscited by 2
  8. 2025Reinforcement Learning to Initialize Newton-Raphson for AC Power Flow with Quantum Annealing-Based Environment Updatescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Physics-Informed but Not Physics-Consistent: Error Geometry and Subspace Projection for Neural AC Power Flowcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Reinforcement Learning for Power-Flow Network Analysis
  2. 2026FMOPF: Latent Flow Matching with Constraint-Aware Interaction Priors for AC Optimal Power Flow
  3. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  4. 2018Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning
  5. 2024Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
  6. 2024End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability
  7. 2025Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
  8. 2025Refining Graphical Neural Network Predictions Using Flow Matching for Optimal Power Flow with Constraint-Satisfaction Guarantee