PWO is a trust-region optimizer for autoregressive NQS that improves stability over Adam and stochastic reconfiguration methods while scaling to 1.5B-parameter models on spin systems.
Deep reinforcement learning at the edge of the statistical precipice
7 Pith papers cite this work. Polarity classification is still indexing.
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2026 7representative citing papers
An LLM-driven agent with built-in seed-noise audits develops control policies for two aerospace problems that outperform undirected search and pass verification checks.
PE-MAMoE combines sparsely gated mixture-of-experts actors with a non-parametric phase controller in MAPPO to maintain plasticity under dynamic user mobility and traffic, yielding 26.3% higher normalized IQM return in simulations.
UBP2 uses ensembles of reward, dynamics, and value models to score trajectories on a unified objective of reward plus uncertainty, yielding sublinear regret bounds and higher sample efficiency on Meta-World than prior preference-based methods.
LQL turns n-step action-sequence lower bounds into a practical hinge-loss stabilizer for off-policy Q-learning without extra networks or forward passes.
Resampling clinical time series into uniform bins for offline RL reduces performance by up to 60% and causes retrospective evaluations to overestimate returns by 1.5-3x versus unprocessed data.
A tutorial playbook that organizes statistical evaluation into a workflow of claim, hypothesis, unit of analysis, baselines, sweeps, uncertainty, validation, and reporting, illustrated with Python code and a job-scheduling example.
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Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities
LQL turns n-step action-sequence lower bounds into a practical hinge-loss stabilizer for off-policy Q-learning without extra networks or forward passes.