arXiv Machine Learning
GRPO-QM: Target Preserving Exploration for Quantum Tomography
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arXiv Machine Learning
Sep 23
GRPO-QPS: Target-Preserving Reinforcement Learning for Quantum Posterior Sampling
arXiv:2609.14711v2 Announce Type: replace Abstract: Bayesian quantum tomography requires efficient inference while preserving a posterior fixed by the prior and Born likelihood. Learned transport pro...
arXiv AI
Aug 3
DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search
arXiv:2607. 29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construction and action legality are deterministic and known.
arXiv Machine Learning
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Generative Replay Mitigates Sample Starvation in Quantum Architecture Search
The paper introduces GenQAS, a tensor network‑guided reinforcement learning framework that uses a learned local transition model to generate synthetic circuit transitions for prioritized generative replay. By mixing these synthetic transitions with real experience during Double Deep Q‑Network updates, GenQAS addresses sample starvation in quantum architecture search. Across benchmarks ranging from 6 to 15 qubits, the method improves success probabilities, identifies compact circuits, and reduces steps to chemical accuracy by up to 92.7%.
arXiv Machine Learning
Sep 21
$\lambda$-Controlled GRPO: Turning Flow-Matching Ratio Instability into a Budgeted Resource
arXiv:2609. 22041v1 Announce Type: new Abstract: Reinforcement learning is increasingly used to align image generators with reward signals, and Flow-GRPO recently extended this paradigm to flow-matching models by treating the denoising sampler as a stochastic policy that can be optimized from reward feedback.
arXiv AI
Aug 6
Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation
arXiv:2608. 04788v1 Announce Type: cross Abstract: Large language model agents are commonly trained through reinforcement learning with sparse trajectory-level rewards, which offer limited guidance on how strongly individual tokens should be updated.
arXiv Computer Vision
3d ago
ExploreNet: Learning Where to Explore in Diffusion GRPO
arXiv:2609.38329v1 Announce Type: new Abstract: Group-relative RL methods such as Flow-GRPO post-train image generators by exploring with isotropic Gaussian noise added at every denoising step. This...