面向自我提升 Agentic AI 的高能物理奖励任务：通过强化学习推动智能体进化
Reward-Driven Collider Physics Challenges for Self-Improving Agentic AI via Reinforcement Learning
AI 导读
一项 arXiv 研究提出以高能物理（HEP）任务为基础的开放式强化学习（RL）环境，采用连续、可自动评估的奖励，让智能体 AI 在前沿模型仍难以完整解决的科学挑战上自我改进。团队对多个前沿推理模型进行了基准测试，并给出概念验证：一个小语言模型借助自动化反馈通过 RL 提升，全程无需人工干预。
正文 · 原文
Abstract:Recent advances in agentic AI have demonstrated the potential to automate complex high-energy physics (HEP) workflows. We focus on the regime of HEP tasks where frontier models still struggle to provide correct and complete solutions, and introduce an open-ended optimization task format with continuous, automatically evaluated rewards. Unlike conventional benchmarks focused on task completion, these tasks target reinforcement learning (RL) environments to generate agentic improvement on state- of-the-art HEP challenges. We benchmark frontier reasoning models on several HEP tasks and demonstrate an initial proof of concept in which a small language model improves through RL using automated feedback without human intervention. By connecting challenging HEP problems with verifiable scientific rewards and RL, we aim to advance agentic AI from reproducing established workflows to autonomously discovering better scientific solutions, paving the way toward self-improving scientific agents.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.15885 [cs.AI] |
| (or arXiv:2611.15885v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15885 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qibin Liu [view email]
[v1]
Wed, 11 Nov 2026 19:00:41 UTC (562 KB)
来源：arXiv cs.AI · arxiv.org