Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (textittriggering) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy (HT) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% (HT) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to real collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% (HT) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the first demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPOLHC (see repo for details).
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 82 references that are in the map. First posted on arXiv 22 Jun 2026.
Builds on
10 of 82 references.
References · 10
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
- 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
- 2019Time2Vec: Learning a Vector Representation of Timecited by 213
- 2020What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Studycited by 162
- 2026Demystifying Group Relative Policy Optimization: Its Policy Gradient is a U-Statisticcited by 21
- 2024Real-time Anomaly Detection at the L1 Trigger of CMS Experimentcited by 7
- 2025Extending Group Relative Policy Optimization to Continuous Control: A Theoretical Framework for Robotic Reinforcement Learningcited by 3
- 2021Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilitiescited by 1
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- 2025Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach
- 2025Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach
- 2024Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments
- 2023Improving new physics searches with diffusion models for event observables and jet constituents
- 2026Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
- 2021A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
- 2023Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation