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Reinforcement learning

Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.

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arXiv AI
Sep 1

Scaling Large Reasoning Models beyond Human Supervision: A Path toward Superintelligence

arXiv:2608.31075v1 Announce Type: new Abstract: Recent advances in large reasoning models (LRMs) have shown that reinforcement learning with verifiable rewards (RLVR) can substantially improve reason...

By Zhiqin Yang, Jingwen Fu, Yuhan Liu, Hengyu Liu, Yonggang Zhang, Kainan Cao, Zizhuo Zhang, Chenxin Li, Ruibin Yuan, Jiahao Pan, Jiankai Sun, Zhenyuan Zhang, Yibo Li, Yunlong Lin, Jing Xiong, Sida Lin, Bo Han, Wei Xue, Yike Guo
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arXiv Machine Learning
Sep 1

AutoREC: A reinforcement learning platform for equivalent circuit model generation

AutoREC is an open‑source Python platform that uses reinforcement learning to automatically generate equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data. The platform frames ECM generation as a Markov decision process, training a Double Deep Q‑Network agent that iteratively modifies circuit topologies based on state, actions, and model feedback. It supports end‑to‑end workflows—including EIS preprocessing, agent training, ECM generation, and visualization—and has been demonstrated on synthetic datasets and real experimental spectra from batteries, corrosion, oxygen evolution, and CO₂ reduction systems.

By Ali Jaberi (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Yonatan Kurniawan (Department of Materials Science and Engineering, University of Toronto, Toronto, ON, Canada), Robert Black (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Shayan Mousavi M. (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Kabir Verma (Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada), Zoya Sadighi (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Santiago Miret (Lila Sciences, San Francisco, CA, USA), Jason Hattrick-Simpers (Department of Materials Science and Engineering, University of Toronto, Toronto, ON, Canada)
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arXiv Machine Learning
Sep 1

Titans-QFWP: A Regime-Aware Hybrid Quantum Fast Weight Programmer for Portfolio Optimization

Titans-QFWP is a hybrid reinforcement learning architecture that combines a Quantum Fast Weight Programmer with a Titans-style memory system (Persistence, Surprise, and Forgetting) for adaptive portfolio optimization. It employs an enhanced A3C² framework with Hungarian-aligned K‑means clustering and scaled log‑return rewards to handle high‑dimensional market features. Tested on 468 S&P 500 stocks with about 3,000 trainable parameters, the model achieves strong performance metrics (median ARR 0.4260, Calmar 8.5504, IR 0.8427) and demonstrates that quantum gating reshapes memory roles to support drawdown control, return generation, and stabilization.

By Ming-Kai Hung, Jun-Hao Chen, Yun-Cheng Tsai, Samuel Yen-Chi Chen
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